{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Python for Algorithmic Trading\n", "\n", "**Appendix A &mdash; Python, NumPy, pandas**"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Python Basics"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Data Types"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["a = 3"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/plain": ["int"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["type(a)"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/plain": ["2"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["a.bit_length()"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": ["b = 5."]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["float"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["type(b)"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["c = 10 ** 100"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/plain": ["10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["c "]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/plain": ["333"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["c.bit_length()"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/plain": ["0.6"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["3 / 5."]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/plain": ["15.0"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["a * b"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/plain": ["-2.0"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["a - b"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"text/plain": ["8.0"]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["b + a"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/plain": ["243.0"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["a ** b"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": ["import math"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"text/plain": ["1.0986122886681098"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["math.log(a)"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/plain": ["20.085536923187668"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["math.exp(a)"]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"text/plain": ["-0.9589242746631385"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["math.sin(b)"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": ["s = 'Python for Algorithmic Trading.'"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/plain": ["str"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["type(s)"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/plain": ["'python for algorithmic trading.'"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["s.lower()"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"text/plain": ["'PYTHON FOR ALGORITHMIC TRADING.'"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["s.upper()"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/plain": ["'Python'"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["s[0:6]"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": ["st = s[0:6] + s[-9:-1]"]}, {"cell_type": "code", "execution_count": 24, "metadata": {"scrolled": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Python Trading\n"]}], "source": ["print(st)"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": ["repl = 'My name is %s, I am %d years old and %4.2f m tall.'"]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["My name is Gordon Gekko, I am 43 years old and 1.78 m tall.\n"]}], "source": ["print(repl % ('Gordon Gekko', 43, 1.78))"]}, {"cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": ["repl = 'My name is {:s}, I am {:d} years old and {:4.2f} m tall.' # <3>"]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["My name is Gordon Gekko, I am 43 years old and 1.78 m tall.\n"]}], "source": ["print(repl.format('Gordon Gekko', 43, 1.78))"]}, {"cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": ["name, age, height = 'Gordon Gekko', 43, 1.78 # <5>"]}, {"cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["My name is Gordon Gekko, I am 43 years old and 1.78m tall.\n"]}], "source": ["print(f'My name is {name:s}, I am {age:d} years old and {height:4.2f}m tall.')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Data Structures"]}, {"cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": ["t1 = (a, b, st)"]}, {"cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [{"data": {"text/plain": ["(3, 5.0, 'Python Trading')"]}, "execution_count": 32, "metadata": {}, "output_type": "execute_result"}], "source": ["t1"]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [{"data": {"text/plain": ["tuple"]}, "execution_count": 33, "metadata": {}, "output_type": "execute_result"}], "source": ["type(t1)"]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": ["t2 = st, b, a"]}, {"cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [{"data": {"text/plain": ["('Python Trading', 5.0, 3)"]}, "execution_count": 35, "metadata": {}, "output_type": "execute_result"}], "source": ["t2"]}, {"cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [{"data": {"text/plain": ["tuple"]}, "execution_count": 36, "metadata": {}, "output_type": "execute_result"}], "source": ["type(t2)"]}, {"cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": ["t = (t1, t2)"]}, {"cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [{"data": {"text/plain": ["((3, 5.0, 'Python Trading'), ('Python Trading', 5.0, 3))"]}, "execution_count": 38, "metadata": {}, "output_type": "execute_result"}], "source": ["t"]}, {"cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [{"data": {"text/plain": ["'Python Trading'"]}, "execution_count": 39, "metadata": {}, "output_type": "execute_result"}], "source": ["t[0][2]"]}, {"cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": ["l = [a, b, st]"]}, {"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [{"data": {"text/plain": ["[3, 5.0, 'Python Trading']"]}, "execution_count": 41, "metadata": {}, "output_type": "execute_result"}], "source": ["l"]}, {"cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [{"data": {"text/plain": ["list"]}, "execution_count": 42, "metadata": {}, "output_type": "execute_result"}], "source": ["type(l)"]}, {"cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": ["l.append(s.split()[3])"]}, {"cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [{"data": {"text/plain": ["[3, 5.0, 'Python Trading', 'Trading.']"]}, "execution_count": 44, "metadata": {}, "output_type": "execute_result"}], "source": ["l"]}, {"cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": ["l = list(('Z', 'Q', 'D', 'J', 'E', 'H', '5.', 'a'))"]}, {"cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [{"data": {"text/plain": ["['Z', 'Q', 'D', 'J', 'E', 'H', '5.', 'a']"]}, "execution_count": 46, "metadata": {}, "output_type": "execute_result"}], "source": ["l"]}, {"cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": ["l.sort()"]}, {"cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [{"data": {"text/plain": ["['5.', 'D', 'E', 'H', 'J', 'Q', 'Z', 'a']"]}, "execution_count": 48, "metadata": {}, "output_type": "execute_result"}], "source": ["l"]}, {"cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": ["d = {'int_obj': a, 'float_obj': b, 'string_obj': st}"]}, {"cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [{"data": {"text/plain": ["dict"]}, "execution_count": 50, "metadata": {}, "output_type": "execute_result"}], "source": ["type(d)"]}, {"cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'int_obj': 3, 'float_obj': 5.0, 'string_obj': 'Python Trading'}"]}, "execution_count": 51, "metadata": {}, "output_type": "execute_result"}], "source": ["d"]}, {"cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [{"data": {"text/plain": ["5.0"]}, "execution_count": 52, "metadata": {}, "output_type": "execute_result"}], "source": ["d['float_obj']"]}, {"cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": ["d['int_obj_long'] = 10 ** 20"]}, {"cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'int_obj': 3,\n", " 'float_obj': 5.0,\n", " 'string_obj': 'Python Trading',\n", " 'int_obj_long': 100000000000000000000}"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["d"]}, {"cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [{"data": {"text/plain": ["dict_keys(['int_obj', 'float_obj', 'string_obj', 'int_obj_long'])"]}, "execution_count": 55, "metadata": {}, "output_type": "execute_result"}], "source": ["d.keys()"]}, {"cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [{"data": {"text/plain": ["dict_values([3, 5.0, 'Python Trading', 100000000000000000000])"]}, "execution_count": 56, "metadata": {}, "output_type": "execute_result"}], "source": ["d.values()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Control Structures"]}, {"cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [{"data": {"text/plain": ["range(0, 5)"]}, "execution_count": 57, "metadata": {}, "output_type": "execute_result"}], "source": ["range(5)"]}, {"cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [{"data": {"text/plain": ["range(3, 15, 2)"]}, "execution_count": 58, "metadata": {}, "output_type": "execute_result"}], "source": ["range(3, 15, 2)"]}, {"cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["0 1 4 9 16 "]}], "source": ["for i in range(5):\n", "    print(i ** 2, end=' ')"]}, {"cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["3 5 7 9 11 13 "]}], "source": ["for i in range(3, 15, 2):\n", "    print(i, end=' ')"]}, {"cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": ["l = ['a', 'b', 'c', 'd', 'e']"]}, {"cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["a\n", "b\n", "c\n", "d\n", "e\n"]}], "source": ["for _ in l:\n", "    print(_)"]}, {"cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": ["s = 'Python Trading'"]}, {"cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["P|y|t|h|o|n| |T|r|a|d|i|n|g|"]}], "source": ["for c in s:\n", "    print(c + '|', end='')"]}, {"cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": ["i = 0"]}, {"cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["0.0 1.0 1.4142135623730951 1.7320508075688772 2.0 "]}], "source": ["while i < 5:\n", "    print(i ** 0.5, end=' ')\n", "    i += 1"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Python Idioms "]}, {"cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": ["lc = [i ** 2 for i in range(10)]"]}, {"cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [{"data": {"text/plain": ["[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"]}, "execution_count": 68, "metadata": {}, "output_type": "execute_result"}], "source": ["lc"]}, {"cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [{"data": {"text/plain": ["list"]}, "execution_count": 69, "metadata": {}, "output_type": "execute_result"}], "source": ["type(lc)"]}, {"cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": ["f = lambda x: math.cos(x)"]}, {"cell_type": "code", "execution_count": 71, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/plain": ["0.2836621854632263"]}, "execution_count": 71, "metadata": {}, "output_type": "execute_result"}], "source": ["f(5)"]}, {"cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [{"data": {"text/plain": ["[1.0,\n", " 0.5403023058681398,\n", " -0.4161468365471424,\n", " -0.9899924966004454,\n", " -0.6536436208636119,\n", " 0.2836621854632263,\n", " 0.9601702866503661,\n", " 0.7539022543433046,\n", " -0.14550003380861354,\n", " -0.9111302618846769]"]}, "execution_count": 72, "metadata": {}, "output_type": "execute_result"}], "source": ["list(map(lambda x: math.cos(x), range(10)))"]}, {"cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": ["def f(x):\n", "    return math.exp(x)"]}, {"cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [{"data": {"text/plain": ["148.4131591025766"]}, "execution_count": 74, "metadata": {}, "output_type": "execute_result"}], "source": ["f(5)"]}, {"cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": ["def f(*args):\n", "    for arg in args:\n", "        print(arg)\n", "    return None"]}, {"cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['a', 'b', 'c', 'd', 'e']\n"]}], "source": ["f(l)"]}, {"cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": ["import random"]}, {"cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": ["a = random.randint(0, 1000)"]}, {"cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Random number is 74\n"]}], "source": ["print(f'Random number is {a}')"]}, {"cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": ["def number_decide(number):\n", "    if a < 10:\n", "        return \"Number is single digit.\"\n", "    elif 10 <= a < 100:\n", "        return \"Number is double digit.\"\n", "    else:\n", "        return \"Number is triple digit.\""]}, {"cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [{"data": {"text/plain": ["'Number is double digit.'"]}, "execution_count": 81, "metadata": {}, "output_type": "execute_result"}], "source": ["number_decide(a)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# NumPy"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Regular ndarray Objects"]}, {"cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": ["import numpy as np"]}, {"cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": ["a = np.array(range(24))"]}, {"cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n", "       17, 18, 19, 20, 21, 22, 23])"]}, "execution_count": 84, "metadata": {}, "output_type": "execute_result"}], "source": ["a"]}, {"cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": ["b = a.reshape((4, 6))"]}, {"cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 0,  1,  2,  3,  4,  5],\n", "       [ 6,  7,  8,  9, 10, 11],\n", "       [12, 13, 14, 15, 16, 17],\n", "       [18, 19, 20, 21, 22, 23]])"]}, "execution_count": 86, "metadata": {}, "output_type": "execute_result"}], "source": ["b"]}, {"cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": ["c = a.reshape((2, 3, 4))"]}, {"cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[[ 0,  1,  2,  3],\n", "        [ 4,  5,  6,  7],\n", "        [ 8,  9, 10, 11]],\n", "\n", "       [[12, 13, 14, 15],\n", "        [16, 17, 18, 19],\n", "        [20, 21, 22, 23]]])"]}, "execution_count": 88, "metadata": {}, "output_type": "execute_result"}], "source": ["c"]}, {"cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": ["b = np.array(b, dtype=np.float)"]}, {"cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 0.,  1.,  2.,  3.,  4.,  5.],\n", "       [ 6.,  7.,  8.,  9., 10., 11.],\n", "       [12., 13., 14., 15., 16., 17.],\n", "       [18., 19., 20., 21., 22., 23.]])"]}, "execution_count": 90, "metadata": {}, "output_type": "execute_result"}], "source": ["b"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Vectorized Operations"]}, {"cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 0.,  2.,  4.,  6.,  8., 10.],\n", "       [12., 14., 16., 18., 20., 22.],\n", "       [24., 26., 28., 30., 32., 34.],\n", "       [36., 38., 40., 42., 44., 46.]])"]}, "execution_count": 91, "metadata": {}, "output_type": "execute_result"}], "source": ["2 * b"]}, {"cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[  0.,   1.,   4.,   9.,  16.,  25.],\n", "       [ 36.,  49.,  64.,  81., 100., 121.],\n", "       [144., 169., 196., 225., 256., 289.],\n", "       [324., 361., 400., 441., 484., 529.]])"]}, "execution_count": 92, "metadata": {}, "output_type": "execute_result"}], "source": ["b ** 2"]}, {"cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": ["f = lambda x: x ** 2 - 2 * x + 0.5"]}, {"cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([  0.5,  -0.5,   0.5,   3.5,   8.5,  15.5,  24.5,  35.5,  48.5,\n", "        63.5,  80.5,  99.5, 120.5, 143.5, 168.5, 195.5, 224.5, 255.5,\n", "       288.5, 323.5, 360.5, 399.5, 440.5, 483.5])"]}, "execution_count": 94, "metadata": {}, "output_type": "execute_result"}], "source": ["f(a)"]}, {"cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([2, 3, 4, 5])"]}, "execution_count": 95, "metadata": {}, "output_type": "execute_result"}], "source": ["a[2:6]"]}, {"cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [{"data": {"text/plain": ["16.0"]}, "execution_count": 96, "metadata": {}, "output_type": "execute_result"}], "source": ["b[2, 4]"]}, {"cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 8.,  9.],\n", "       [14., 15.]])"]}, "execution_count": 97, "metadata": {}, "output_type": "execute_result"}], "source": ["b[1:3, 2:4]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Boolean Operations"]}, {"cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[False, False, False, False, False, False],\n", "       [False, False, False, False, False,  True],\n", "       [ True,  True,  True,  True,  True,  True],\n", "       [ True,  True,  True,  True,  True,  True]])"]}, "execution_count": 98, "metadata": {}, "output_type": "execute_result"}], "source": ["b > 10"]}, {"cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.])"]}, "execution_count": 99, "metadata": {}, "output_type": "execute_result"}], "source": ["b[b > 10]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## `ndarray` Methods and NumPy Functions"]}, {"cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [{"data": {"text/plain": ["276"]}, "execution_count": 100, "metadata": {}, "output_type": "execute_result"}], "source": ["a.sum()"]}, {"cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [{"data": {"text/plain": ["11.5"]}, "execution_count": 101, "metadata": {}, "output_type": "execute_result"}], "source": ["b.mean()"]}, {"cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([ 9., 10., 11., 12., 13., 14.])"]}, "execution_count": 102, "metadata": {}, "output_type": "execute_result"}], "source": ["b.mean(axis=0)"]}, {"cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([ 2.5,  8.5, 14.5, 20.5])"]}, "execution_count": 103, "metadata": {}, "output_type": "execute_result"}], "source": ["b.mean(axis=1)"]}, {"cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [{"data": {"text/plain": ["6.922186552431729"]}, "execution_count": 104, "metadata": {}, "output_type": "execute_result"}], "source": ["c.std()"]}, {"cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [{"data": {"text/plain": ["276"]}, "execution_count": 105, "metadata": {}, "output_type": "execute_result"}], "source": ["np.sum(a)"]}, {"cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([ 9., 10., 11., 12., 13., 14.])"]}, "execution_count": 106, "metadata": {}, "output_type": "execute_result"}], "source": ["np.mean(b, axis=0)"]}, {"cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[ 0.  ,  0.84,  0.91,  0.14, -0.76, -0.96],\n", "       [-0.28,  0.66,  0.99,  0.41, -0.54, -1.  ],\n", "       [-0.54,  0.42,  0.99,  0.65, -0.29, -0.96],\n", "       [-0.75,  0.15,  0.91,  0.84, -0.01, -0.85]])"]}, "execution_count": 107, "metadata": {}, "output_type": "execute_result"}], "source": ["np.sin(b).round(2)"]}, {"cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [{"data": {"text/plain": ["-0.977530117665097"]}, "execution_count": 108, "metadata": {}, "output_type": "execute_result"}], "source": ["np.sin(4.5)"]}, {"cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 1.21 s, sys: 24.1 ms, total: 1.23 s\n", "Wall time: 1.26 s\n"]}], "source": ["%time l = [np.sin(x) for x in range(1000000)]"]}, {"cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 205 ms, sys: 19.7 ms, total: 225 ms\n", "Wall time: 223 ms\n"]}], "source": ["%time l = [math.sin(x) for x in range(1000000)]"]}, {"cell_type": "code", "execution_count": 111, "metadata": {"scrolled": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 21.2 ms, sys: 4.77 ms, total: 26 ms\n", "Wall time: 25 ms\n"]}], "source": ["%time a = np.sin(np.arange(1000000))"]}, {"cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": ["import sys"]}, {"cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [{"data": {"text/plain": ["8000096"]}, "execution_count": 113, "metadata": {}, "output_type": "execute_result"}], "source": ["sys.getsizeof(a)"]}, {"cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [{"data": {"text/plain": ["8000000"]}, "execution_count": 114, "metadata": {}, "output_type": "execute_result"}], "source": ["a.nbytes"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## ndarray Creation"]}, {"cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": ["ai = np.arange(10)"]}, {"cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"]}, "execution_count": 116, "metadata": {}, "output_type": "execute_result"}], "source": ["ai"]}, {"cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [{"data": {"text/plain": ["dtype('int64')"]}, "execution_count": 117, "metadata": {}, "output_type": "execute_result"}], "source": ["ai.dtype"]}, {"cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": ["af = np.arange(0.5, 9.5, 0.5)"]}, {"cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5,\n", "       7. , 7.5, 8. , 8.5, 9. ])"]}, "execution_count": 119, "metadata": {}, "output_type": "execute_result"}], "source": ["af"]}, {"cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [{"data": {"text/plain": ["dtype('float64')"]}, "execution_count": 120, "metadata": {}, "output_type": "execute_result"}], "source": ["af.dtype"]}, {"cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([ 0.        ,  0.90909091,  1.81818182,  2.72727273,  3.63636364,\n", "        4.54545455,  5.45454545,  6.36363636,  7.27272727,  8.18181818,\n", "        9.09090909, 10.        ])"]}, "execution_count": 121, "metadata": {}, "output_type": "execute_result"}], "source": ["np.linspace(0, 10, 12)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Random Numbers"]}, {"cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([-2.1522647 ,  2.13160089,  0.56002594, -0.79347248, -0.72049978,\n", "        2.53017257, -1.31409766, -3.32250941, -2.38801578, -1.53071788])"]}, "execution_count": 122, "metadata": {}, "output_type": "execute_result"}], "source": ["np.random.standard_normal(10) "]}, {"cell_type": "code", "execution_count": 123, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/plain": ["array([0, 1, 2, 0, 1, 1, 0, 1, 0, 1])"]}, "execution_count": 123, "metadata": {}, "output_type": "execute_result"}], "source": ["np.random.poisson(0.5, 10)"]}, {"cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": ["np.random.seed(1000)"]}, {"cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [], "source": ["data = np.random.standard_normal((5, 100))"]}, {"cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[-0.8044583 ,  0.32093155, -0.02548288],\n", "       [-0.39031935, -0.58069634,  1.94898697],\n", "       [-1.11573322, -1.34477121,  0.75334374],\n", "       [ 0.42400699, -1.56680276,  0.76499895],\n", "       [-1.74866738, -0.06913021,  1.52621653]])"]}, "execution_count": 126, "metadata": {}, "output_type": "execute_result"}], "source": ["data[:, :3]"]}, {"cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [{"data": {"text/plain": ["-0.02714981205311327"]}, "execution_count": 127, "metadata": {}, "output_type": "execute_result"}], "source": ["data.mean()"]}, {"cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [{"data": {"text/plain": ["1.0016799134894265"]}, "execution_count": 128, "metadata": {}, "output_type": "execute_result"}], "source": ["data.std()"]}, {"cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": ["data = data - data.mean()"]}, {"cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [{"data": {"text/plain": ["3.552713678800501e-18"]}, "execution_count": 130, "metadata": {}, "output_type": "execute_result"}], "source": ["data.mean()"]}, {"cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [], "source": ["data = data / data.std()"]}, {"cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [{"data": {"text/plain": ["1.0"]}, "execution_count": 132, "metadata": {}, "output_type": "execute_result"}], "source": ["data.std()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# matplotlib"]}, {"cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt"]}, {"cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": ["plt.style.use('seaborn')"]}, {"cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": ["import matplotlib as mpl"]}, {"cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": ["mpl.rcParams['font.family'] = 'serif'\n", "%matplotlib inline"]}, {"cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": ["data = np.random.standard_normal((5, 100))"]}, {"cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(10, 6))\n", "plt.plot(data.cumsum())\n", "# plt.savefig('../../images/chA/plot_01.png');"]}, {"cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(10, 6));\n", "plt.plot(data.T.cumsum(axis=0), label='line')\n", "plt.legend(loc=0);\n", "plt.xlabel('data point')\n", "plt.ylabel('value');\n", "plt.title('random series');\n", "# plt.savefig('../../images/chA/plot_02.png');"]}, {"cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(10, 6))\n", "plt.hist(data.flatten(), bins=30);\n", "# plt.savefig('../../images/chA/plot_03.png');"]}, {"cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(10, 6))\n", "plt.bar(np.arange(1, 12) - 0.25,\n", "        data[0, :11], width=0.5);\n", "# plt.savefig('../../images/chA/plot_04.png');"]}, {"cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [], "source": ["x = np.arange(len(data.cumsum()))"]}, {"cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": ["y = 0.2 * data.cumsum() ** 2 # <2>"]}, {"cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [], "source": ["rg1 = np.polyfit(x, y, 1)"]}, {"cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": ["rg3 = np.polyfit(x, y, 3)"]}, {"cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [], "source": ["rg9 = np.polyfit(x, y, 9)"]}, {"cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(10, 6))\n", "plt.plot(x, y, 'r', label='data')\n", "plt.plot(x, np.polyval(rg1, x), 'b--', label='linear')\n", "plt.plot(x, np.polyval(rg3, x), 'b-.', label='cubic')\n", "plt.plot(x, np.polyval(rg9, x), 'b:', label='7th degree')\n", "plt.legend(loc=0);\n", "# plt.savefig('../../images/chA/plot_05.png');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# pandas"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## pandas DataFrame class"]}, {"cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [], "source": ["import pandas as pd "]}, {"cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [], "source": ["np.random.seed(1000)"]}, {"cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [], "source": ["raw = np.random.standard_normal((10, 3)).cumsum(axis=0)"]}, {"cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [], "source": ["index = pd.date_range('2022-1-1', periods=len(raw), freq='M')"]}, {"cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [], "source": ["columns = ['no1', 'no2', 'no3']"]}, {"cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [], "source": ["df = pd.DataFrame(raw, index=index, columns=columns)"]}, {"cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-01-31</th>\n", "      <td>-0.804458</td>\n", "      <td>0.320932</td>\n", "      <td>-0.025483</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-02-28</th>\n", "      <td>-0.160134</td>\n", "      <td>0.020135</td>\n", "      <td>0.363992</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-03-31</th>\n", "      <td>-0.267572</td>\n", "      <td>-0.459848</td>\n", "      <td>0.959027</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-04-30</th>\n", "      <td>-0.732239</td>\n", "      <td>0.207433</td>\n", "      <td>0.152912</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-05-31</th>\n", "      <td>-1.928309</td>\n", "      <td>-0.198527</td>\n", "      <td>-0.029466</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-06-30</th>\n", "      <td>-1.825116</td>\n", "      <td>-0.336949</td>\n", "      <td>0.676227</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-07-31</th>\n", "      <td>-0.553321</td>\n", "      <td>-1.323696</td>\n", "      <td>0.341391</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-08-31</th>\n", "      <td>-0.652803</td>\n", "      <td>-0.916504</td>\n", "      <td>1.260779</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-09-30</th>\n", "      <td>-0.340685</td>\n", "      <td>0.616657</td>\n", "      <td>0.710605</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-10-31</th>\n", "      <td>-0.723832</td>\n", "      <td>-0.206284</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-01-31 -0.804458  0.320932 -0.025483\n", "2022-02-28 -0.160134  0.020135  0.363992\n", "2022-03-31 -0.267572 -0.459848  0.959027\n", "2022-04-30 -0.732239  0.207433  0.152912\n", "2022-05-31 -1.928309 -0.198527 -0.029466\n", "2022-06-30 -1.825116 -0.336949  0.676227\n", "2022-07-31 -0.553321 -1.323696  0.341391\n", "2022-08-31 -0.652803 -0.916504  1.260779\n", "2022-09-30 -0.340685  0.616657  0.710605\n", "2022-10-31 -0.723832 -0.206284  2.310688"]}, "execution_count": 154, "metadata": {}, "output_type": "execute_result"}], "source": ["df"]}, {"cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-01-31</th>\n", "      <td>-0.804458</td>\n", "      <td>0.320932</td>\n", "      <td>-0.025483</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-02-28</th>\n", "      <td>-0.160134</td>\n", "      <td>0.020135</td>\n", "      <td>0.363992</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-03-31</th>\n", "      <td>-0.267572</td>\n", "      <td>-0.459848</td>\n", "      <td>0.959027</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-04-30</th>\n", "      <td>-0.732239</td>\n", "      <td>0.207433</td>\n", "      <td>0.152912</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-05-31</th>\n", "      <td>-1.928309</td>\n", "      <td>-0.198527</td>\n", "      <td>-0.029466</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-01-31 -0.804458  0.320932 -0.025483\n", "2022-02-28 -0.160134  0.020135  0.363992\n", "2022-03-31 -0.267572 -0.459848  0.959027\n", "2022-04-30 -0.732239  0.207433  0.152912\n", "2022-05-31 -1.928309 -0.198527 -0.029466"]}, "execution_count": 155, "metadata": {}, "output_type": "execute_result"}], "source": ["df.head()"]}, {"cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-06-30</th>\n", "      <td>-1.825116</td>\n", "      <td>-0.336949</td>\n", "      <td>0.676227</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-07-31</th>\n", "      <td>-0.553321</td>\n", "      <td>-1.323696</td>\n", "      <td>0.341391</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-08-31</th>\n", "      <td>-0.652803</td>\n", "      <td>-0.916504</td>\n", "      <td>1.260779</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-09-30</th>\n", "      <td>-0.340685</td>\n", "      <td>0.616657</td>\n", "      <td>0.710605</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-10-31</th>\n", "      <td>-0.723832</td>\n", "      <td>-0.206284</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-06-30 -1.825116 -0.336949  0.676227\n", "2022-07-31 -0.553321 -1.323696  0.341391\n", "2022-08-31 -0.652803 -0.916504  1.260779\n", "2022-09-30 -0.340685  0.616657  0.710605\n", "2022-10-31 -0.723832 -0.206284  2.310688"]}, "execution_count": 156, "metadata": {}, "output_type": "execute_result"}], "source": ["df.tail()"]}, {"cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2022-01-31', '2022-02-28', '2022-03-31', '2022-04-30',\n", "               '2022-05-31', '2022-06-30', '2022-07-31', '2022-08-31',\n", "               '2022-09-30', '2022-10-31'],\n", "              dtype='datetime64[ns]', freq='M')"]}, "execution_count": 157, "metadata": {}, "output_type": "execute_result"}], "source": ["df.index"]}, {"cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [{"data": {"text/plain": ["Index(['no1', 'no2', 'no3'], dtype='object')"]}, "execution_count": 158, "metadata": {}, "output_type": "execute_result"}], "source": ["df.columns"]}, {"cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 10 entries, 2022-01-31 to 2022-10-31\n", "Freq: M\n", "Data columns (total 3 columns):\n", " #   Column  Non-Null Count  Dtype  \n", "---  ------  --------------  -----  \n", " 0   no1     10 non-null     float64\n", " 1   no2     10 non-null     float64\n", " 2   no3     10 non-null     float64\n", "dtypes: float64(3)\n", "memory usage: 320.0 bytes\n"]}], "source": ["df.info()"]}, {"cell_type": "code", "execution_count": 160, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>count</th>\n", "      <td>10.000000</td>\n", "      <td>10.000000</td>\n", "      <td>10.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mean</th>\n", "      <td>-0.798847</td>\n", "      <td>-0.227665</td>\n", "      <td>0.672067</td>\n", "    </tr>\n", "    <tr>\n", "      <th>std</th>\n", "      <td>0.607430</td>\n", "      <td>0.578071</td>\n", "      <td>0.712430</td>\n", "    </tr>\n", "    <tr>\n", "      <th>min</th>\n", "      <td>-1.928309</td>\n", "      <td>-1.323696</td>\n", "      <td>-0.029466</td>\n", "    </tr>\n", "    <tr>\n", "      <th>25%</th>\n", "      <td>-0.786404</td>\n", "      <td>-0.429123</td>\n", "      <td>0.200031</td>\n", "    </tr>\n", "    <tr>\n", "      <th>50%</th>\n", "      <td>-0.688317</td>\n", "      <td>-0.202406</td>\n", "      <td>0.520109</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75%</th>\n", "      <td>-0.393844</td>\n", "      <td>0.160609</td>\n", "      <td>0.896922</td>\n", "    </tr>\n", "    <tr>\n", "      <th>max</th>\n", "      <td>-0.160134</td>\n", "      <td>0.616657</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             no1        no2        no3\n", "count  10.000000  10.000000  10.000000\n", "mean   -0.798847  -0.227665   0.672067\n", "std     0.607430   0.578071   0.712430\n", "min    -1.928309  -1.323696  -0.029466\n", "25%    -0.786404  -0.429123   0.200031\n", "50%    -0.688317  -0.202406   0.520109\n", "75%    -0.393844   0.160609   0.896922\n", "max    -0.160134   0.616657   2.310688"]}, "execution_count": 160, "metadata": {}, "output_type": "execute_result"}], "source": ["df.describe()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Numerical Operations"]}, {"cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["                 no1       no2       no3\n", "2022-01-31 -1.608917  0.641863 -0.050966\n", "2022-02-28 -0.320269  0.040270  0.727983\n", "2022-03-31 -0.535144 -0.919696  1.918054\n", "2022-04-30 -1.464479  0.414866  0.305823\n", "2022-05-31 -3.856618 -0.397054 -0.058932\n", "2022-06-30 -3.650232 -0.673898  1.352453\n", "2022-07-31 -1.106642 -2.647393  0.682782\n", "2022-08-31 -1.305605 -1.833009  2.521557\n", "2022-09-30 -0.681369  1.233314  1.421210\n", "2022-10-31 -1.447664 -0.412568  4.621376\n"]}], "source": ["print(df * 2)"]}, {"cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [{"data": {"text/plain": ["no1    0.607430\n", "no2    0.578071\n", "no3    0.712430\n", "dtype: float64"]}, "execution_count": 162, "metadata": {}, "output_type": "execute_result"}], "source": ["df.std()"]}, {"cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [{"data": {"text/plain": ["no1   -0.798847\n", "no2   -0.227665\n", "no3    0.672067\n", "dtype: float64"]}, "execution_count": 163, "metadata": {}, "output_type": "execute_result"}], "source": ["df.mean()"]}, {"cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [{"data": {"text/plain": ["2022-01-31   -0.169670\n", "2022-02-28    0.074664\n", "2022-03-31    0.077202\n", "2022-04-30   -0.123965\n", "2022-05-31   -0.718767\n", "2022-06-30   -0.495280\n", "2022-07-31   -0.511875\n", "2022-08-31   -0.102843\n", "2022-09-30    0.328859\n", "2022-10-31    0.460191\n", "Freq: M, dtype: float64"]}, "execution_count": 164, "metadata": {}, "output_type": "execute_result"}], "source": ["df.mean(axis=1)"]}, {"cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [{"data": {"text/plain": ["no1   -0.798847\n", "no2   -0.227665\n", "no3    0.672067\n", "dtype: float64"]}, "execution_count": 165, "metadata": {}, "output_type": "execute_result"}], "source": ["np.mean(df)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Data Selection"]}, {"cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [{"data": {"text/plain": ["2022-01-31    0.320932\n", "2022-02-28    0.020135\n", "2022-03-31   -0.459848\n", "2022-04-30    0.207433\n", "2022-05-31   -0.198527\n", "2022-06-30   -0.336949\n", "2022-07-31   -1.323696\n", "2022-08-31   -0.916504\n", "2022-09-30    0.616657\n", "2022-10-31   -0.206284\n", "Freq: M, Name: no2, dtype: float64"]}, "execution_count": 166, "metadata": {}, "output_type": "execute_result"}], "source": ["df['no2']"]}, {"cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [{"data": {"text/plain": ["no1   -0.804458\n", "no2    0.320932\n", "no3   -0.025483\n", "Name: 2022-01-31 00:00:00, dtype: float64"]}, "execution_count": 167, "metadata": {}, "output_type": "execute_result"}], "source": ["df.iloc[0]"]}, {"cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-03-31</th>\n", "      <td>-0.267572</td>\n", "      <td>-0.459848</td>\n", "      <td>0.959027</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-04-30</th>\n", "      <td>-0.732239</td>\n", "      <td>0.207433</td>\n", "      <td>0.152912</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-03-31 -0.267572 -0.459848  0.959027\n", "2022-04-30 -0.732239  0.207433  0.152912"]}, "execution_count": 168, "metadata": {}, "output_type": "execute_result"}], "source": ["df.iloc[2:4]"]}, {"cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [{"data": {"text/plain": ["2022-03-31   -0.459848\n", "2022-04-30    0.207433\n", "Freq: M, Name: no2, dtype: float64"]}, "execution_count": 169, "metadata": {}, "output_type": "execute_result"}], "source": ["df.iloc[2:4, 1]"]}, {"cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [{"data": {"text/plain": ["2022-04-30    0.152912\n", "2022-05-31   -0.029466\n", "2022-06-30    0.676227\n", "2022-07-31    0.341391\n", "Freq: M, Name: no3, dtype: float64"]}, "execution_count": 170, "metadata": {}, "output_type": "execute_result"}], "source": ["df.no3.iloc[3:7]"]}, {"cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [{"data": {"text/plain": ["no1   -0.267572\n", "no2   -0.459848\n", "no3    0.959027\n", "Name: 2022-03-31 00:00:00, dtype: float64"]}, "execution_count": 171, "metadata": {}, "output_type": "execute_result"}], "source": ["df.loc['2022-3-31']"]}, {"cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [{"data": {"text/plain": ["-0.02946577492329111"]}, "execution_count": 172, "metadata": {}, "output_type": "execute_result"}], "source": ["df.loc['2022-5-31', 'no3']"]}, {"cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [{"data": {"text/plain": ["2022-01-31   -0.880907\n", "2022-02-28    0.931841\n", "2022-03-31    2.609510\n", "2022-04-30   -0.273505\n", "2022-05-31   -2.016706\n", "2022-06-30    0.203564\n", "2022-07-31    0.470852\n", "2022-08-31    3.129533\n", "2022-09-30    1.791130\n", "2022-10-31    6.208233\n", "Freq: M, dtype: float64"]}, "execution_count": 173, "metadata": {}, "output_type": "execute_result"}], "source": ["df['no1'] + 3 * df['no3']"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Boolean Operations"]}, {"cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [{"data": {"text/plain": ["2022-01-31    False\n", "2022-02-28    False\n", "2022-03-31     True\n", "2022-04-30    False\n", "2022-05-31    False\n", "2022-06-30     True\n", "2022-07-31    False\n", "2022-08-31     True\n", "2022-09-30     True\n", "2022-10-31     True\n", "Freq: M, Name: no3, dtype: bool"]}, "execution_count": 174, "metadata": {}, "output_type": "execute_result"}], "source": ["df['no3'] > 0.5"]}, {"cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-03-31</th>\n", "      <td>-0.267572</td>\n", "      <td>-0.459848</td>\n", "      <td>0.959027</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-06-30</th>\n", "      <td>-1.825116</td>\n", "      <td>-0.336949</td>\n", "      <td>0.676227</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-08-31</th>\n", "      <td>-0.652803</td>\n", "      <td>-0.916504</td>\n", "      <td>1.260779</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-09-30</th>\n", "      <td>-0.340685</td>\n", "      <td>0.616657</td>\n", "      <td>0.710605</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-10-31</th>\n", "      <td>-0.723832</td>\n", "      <td>-0.206284</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-03-31 -0.267572 -0.459848  0.959027\n", "2022-06-30 -1.825116 -0.336949  0.676227\n", "2022-08-31 -0.652803 -0.916504  1.260779\n", "2022-09-30 -0.340685  0.616657  0.710605\n", "2022-10-31 -0.723832 -0.206284  2.310688"]}, "execution_count": 175, "metadata": {}, "output_type": "execute_result"}], "source": ["df[df['no3'] > 0.5]"]}, {"cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-09-30</th>\n", "      <td>-0.340685</td>\n", "      <td>0.616657</td>\n", "      <td>0.710605</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-10-31</th>\n", "      <td>-0.723832</td>\n", "      <td>-0.206284</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-09-30 -0.340685  0.616657  0.710605\n", "2022-10-31 -0.723832 -0.206284  2.310688"]}, "execution_count": 176, "metadata": {}, "output_type": "execute_result"}], "source": ["df[(df.no3 > 0.5) & (df.no2 > -0.25)]"]}, {"cell_type": "code", "execution_count": 177, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-05-31</th>\n", "      <td>-1.928309</td>\n", "      <td>-0.198527</td>\n", "      <td>-0.029466</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-06-30</th>\n", "      <td>-1.825116</td>\n", "      <td>-0.336949</td>\n", "      <td>0.676227</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-07-31</th>\n", "      <td>-0.553321</td>\n", "      <td>-1.323696</td>\n", "      <td>0.341391</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-08-31</th>\n", "      <td>-0.652803</td>\n", "      <td>-0.916504</td>\n", "      <td>1.260779</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-09-30</th>\n", "      <td>-0.340685</td>\n", "      <td>0.616657</td>\n", "      <td>0.710605</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-10-31</th>\n", "      <td>-0.723832</td>\n", "      <td>-0.206284</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-05-31 -1.928309 -0.198527 -0.029466\n", "2022-06-30 -1.825116 -0.336949  0.676227\n", "2022-07-31 -0.553321 -1.323696  0.341391\n", "2022-08-31 -0.652803 -0.916504  1.260779\n", "2022-09-30 -0.340685  0.616657  0.710605\n", "2022-10-31 -0.723832 -0.206284  2.310688"]}, "execution_count": 177, "metadata": {}, "output_type": "execute_result"}], "source": ["df[df.index > '2022-5-15']"]}, {"cell_type": "code", "execution_count": 178, "metadata": {}, "outputs": [{"data": {"image/png": 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TRI8eg7O0hOJ//J2K997FU1/fwlX7vppl3+KpqyP2okl+EbS8QVa2hBBCiJ9wVpRTPO9JPGYzydffRPToMWf9XNuRI1R8MB9naQkBBgOJM6/BMHQYOl2TFkdaBVdtLQWPPYI+LIz2f38KfWio1iWdFVnZEkIIIbzIZTRS8sw8PGYzibOuPaegBRDWuTPt/vhnEqbPQHE4KH/rdUr/+TTO8vJmqth/VC/+AtXpJGHqFX4TtLxBwpYQQgjxA1dNDSXPzsNdW0PClTOJHTv+vI6jCwwkbtJksp/4OxG9emPNO0jhn/9A9VeLUFwuL1ftHxzFxdRt2khwegZRI0dpXU6LkrAlhBBCAG6ziZJn5+GqMhI/9XLiLr6kyccMSkwk7d77SZ17F/rISKq/WkThn/+I9VCeFyr2L8bPPvmhgenMVt3A9Eza1mcrhBBCnIHHYqHk2adxVZQTO2kycZdO8dqxdTodhgGDyP7rk8SMm4CrsoKSZ57i5Fuv466r89p5fFnD/n1YD+wnvFsPwnv00rqcFidhSwghRJvmaWig5J9P4ywrJWbcBBKuuLJZNrMHhIWRdM1ssh57nJB22Vi2bKbgD49iXr8OVVG8fj5foSoKxk8bG5gmzJjZJm8UkLAlhBCizVLsNkqffxZHcRHRoy8g8epZzR4GQrOzyXrsTyRePRsUDxXvvUPxvCdxlJY063m1Urd5I87SEqKGjSA0q53W5WhCWj8IIYRokxSHg9J/PYvtyGGiho0g+aZbWnwvkau2FuOCD6nfuQMCAoidcBHxl01FHxLSonU0F8Xh4MRjj6BYrWT/7R8ExcVpXdJ5kdYPQgghxDlSXE7KXnoB25HDRA4cTPKNN2uyaTsoNpa0uXeTdu9vCIyNpfa7pRQ8/hj1e3NbvJbmULv8OzwmE7ETL/LboOUNsrIlhBCiTVHdbspeeZGGvblE9O1H2h13+cQsQ8XhoPrrxdSuWAYeD5EDBpJ0zWwCY2K1Lu28uM0mTvz+EfTBIbR/8in0oWFal3Temrqy5ZWvrpycnBTgb0Cf/Pz8M88zEEIIITSmejycfP1VGvbmEt6jJ6m33+kTQQtAHxJC4pUziRo6jIr351O/cwfWA/uJv3w6MReO87t2CdWLF6E6HMTPuMqvg5Y3eOtfbiSwGGh7txgIIYTwC6qiUP72G9Tv2klYTlfS7rwHfVCQ1mX9j5CMTDIf+T1J198I+gCMH39I0d//gr2gQOvSzpqjtBTzhnUEp6QSPercOvC3Rl4JW/n5+Z8Bcl1QCCGET1IVhYr33sGybSuhHTuRfs9vfHoTuk6vJ2b0BWT/7UkMQ4fhKCyg6O9PUPnxh3hsNq3L+1VVPzQwTZhxFbqAAK3L0Zxma6eJiQatTi2EEKINUVWV46+9Sd3GDUR26kiPv/yJwIgIrcs6O4kGUh99EFPuXo79+3VMq1Zg3b2T9rfeTPywoT7Zs8qUu5eGfXuJ7tWT7HEjfbLGlqZZ2JIN8m2Dq6YaXWAQgVFRWpcihGiDVFWl6tNPqF3+HcEZmSTffT+1VgWsfvYzKK09GX98gtpvl1Kz9Bvyn3qGiN59SJp1LUEJiVpXd4qqKBS98Q4A0dOupKqqXuOKvKOpC0S+sStQtDqqx0Ptsm+p/moRutBQ0u99gLAOHbQuSwjRxlQv/rIxaKWkkvHAQwRERmpd0nnTBwUTP2UahsFDqfhgPg17cyk4lEf8ZdOInTDRJzb6W7ZuwVFchGHoMELbZWtdjs/wSuuHnJycMcD1wMXAq8Cz+fn5v3RRWVo/tGKOsjLK334DR8EJAgwGPPX16IKDSbvzHiJ69NS6PCFEG1Gz9BuqvviMoMQkMh951G9bKJyJqqpYtm7BuPBjPBYLwekZJF93A2GdOmtWk+JwUPCHR/FY6sj++1MExcdrVou3NbX1g/TZEl6jKgq1y7+jetEXqG43hmHDSbp6NrbDhzj52quoqkrKzXOIGjJM61KFEK1c7YplGD/5mMC4eDIfeZSg+AStS2oWnvp6qr74FPP6dQBEj76AhCuu1GQFr3rJ11R/+TmxkyaTOH1Gi5+/OUnYEj7BWX6S8rffxH78GAFRUSRfdyOR/fqf+rj1cD5lL/4LxWYj8epZxI6fqGG1QojWzLR2NZUfvEdATAyZD/+e4KQkrUtqdrYjR6j4YD7O0hICDAYSZ16DYeiwFtuc7q6ro+D3D6MLDCL7/54iIDy8Rc7bUiRsCU2pioJp5QqqvvwM1eXCMHgoSbOuPeO7KkdxMSX/ehaP2UTcJZcSf/l0uUtFCOFV5k0bqHjnLQIMUWQ+/DuCU9O0LqnFqG43tSuWUf31YlSnk/Bu3UmafT3BKSnNfu6KD9/DvGY1SbOuJWbs+GY/X0uTsCU046wop/ydt7AfPUKAwUDStTdgGDDwF5/jMhop+dczuCoqiBo5iuTrbpQeLEIIr6j7fivlb7yGPjyczAd/R0hmptYlacJlNFL50fs07NuLLjCQuEsuJXbS5GZr4Oo8WUbB438gKDGJ7Cf+5hMb9b1NwpZocaqiYFq9iqovPkV1OokcOIik2dcRaDi79g7uujpKn/8njsICIvr2I/W2ueiDg5u5aiFEa2bZtZOT/34ZfUgIGb99hNDsbK1L0pSqqtTv2tHYBNVkIig5heRrrye8W3evn6v0pedp2LObtLvuIbLfAK8f3xdI2BItymmspOKdt7AdzkcfGUny7OsxDBp8zsdR7DbKXn4Ja94Bwjp3Ie3u+wjwlyaDQgif0rBvL6UvPY8uMJCM+x/U9I48X+Ox2ahe9AWm1StBVTEMHUbizGu81vvQeiiPkmeeIqxzFzIefrTVbg2RsCVahKoomNeuxvj5p6gOB5H9BpB07fUERkef/zHdbsrfeh3L9u8JTs8g4/7ftqpbs4UQzc+ad5DSF54DIP2+Bwjv2k3jinyTvaCAivffxVFYgD48goQrZxA9cnSThlurikLR3/+Co7CAzN//qVX3UpSwJZqdq8pI+btvYzuUhz48gqTZ12EYPMQr72BURcG44CNMq1cSGB9Pxv0PEpyS6oWqhRCtne3IYUqeewYUhbR7fiN9/H7Ff7aAVC/6HMVuJ7RTZ5Kvu4GQ9IzzOl7dti2Uv/EahsFDSb3tDi9X61skbIlmo6oq5vVrMS78BNVhJ6JvP5KvvYHAmBivn6dmyddUL/qCgEgD6ffdT2j71vsOSQjRdLbjxyn95zwUl4u0uXcT2bef1iX5DVdtLcYFH1K/cwcEBBA74SLiL5t6ToO5FZeTgscexVNnJvtvT/rUyKDmIGFLNAtXdTUV89/GevAA+vBwkq65ttl7tpjWr6Xy/fnSbV4I8YvsRYWUPPMUis1G6u1zMQw8932jAur37qHyow9wV1URmJBA0qzriOzd56yeW/PtUqo+X0jsRReTOOPqZq5UexK2hFepqkrdxvUYP/kYxW4noldvkm+4qcX2Ull27aT89f90m7+VqCFDW+S8Qgj/4CgrpWTeP/A01DdOpBg2QuuS/JricFD99WJqVywDj4fIAQNJvHo2QbE//z3fY7Fw4vcPg15P+/+b1yZubpKwJbzGVVNDxXvvYN2/D31YGIlXzyJq+MgWv7vEmn+Ispee/6Hb/Gxix09o0fMLIXyTs6Kc4nlP4jGbSbr+RmJGX6B1Sa2Go6SYivfnYz92FH1oKPHTphMzdtwZN9BXfvQBptUr29Q0EAlboslUVaVu80aMCz5CsdkI79GT5BtuIihOuyGijuKiH7rNm6XbvBACV5WR4nlP4q6pIfGa2cSOkzdh3qYqCuaN66n67FMUawMh7bJJvu7G03qWOSvKKfjTYwTFxZP91/9rlQ1Mz0TClmgSt6mWivfepWFvLvrQUBJnXkPUqNE+EWxcRiMlzz2Dq7KCqJGjSb7uBuk2L0Qb5KqpoWTek7iqjCRMn0ncpEu0LqlVc9fVYVz4MZatW0CnI2bseOKnXUFAWBhlr7xI/a6dpM69C8OAQVqX2mIkbInzoqoqlq1bqPz4AxSrlfBu3Um+8WaC4hO0Lu000m1eiLbNbTZTPO9JXBXlxE+ZRvyUaVqX1GZY8w5S8cF8XBUVBMTEEDPmQqoXf0lox05k/u4xn3hT3lIkbIlz5jabqHh/Pg17dqMLCSFxxlVEj7nQZ184jd3mX8Sad7Cx2/w99xEQ3vo3ZArR1nksFoqfeQpnaQmxF19CwvQZPvt9qrVSXE5qv11KzdJvUN1uADIf/QNhHTtpXFnLkrAlzpqqqli+30blR++jNDQQ1rUbKTfcTFCi7/dHUVwuyt96g/od0m1eiLbAY22g5Jl5OIoKiRk3gcSrZ0nQ0pCzvJyqzz8lKCWFxOkztC6nxUnYEmfFXVdH5Qfzqd+1E11wMIlXziT6grFNGtXQ0hq7zX+IafWqH7rNP0RwSorWZQlBfe4eVJeLiD590AfJZe6mUuw2Sv75DPbjx4gePYak626UoCU0JWFL/CrLju+p/OB9PPUWwjp3IfmmOQQnJWld1nmRbvPCl6huN8aFH2NavQoAfXgEhiFDiB4xipB22RIQzoPicFD6/D+xHc7HMGw4KTfN8as3haJ1krAlfpbHYqHiw/ep3/E9uuBgEq6Y8bN9U/yNad1aKj+QbvNCO26ziZP/fgXbkcMEp2cQ0bMndVu34DGbAQhOzyBq+Aiihg5v0sD2tkRxOSl74XmseQeIHDiI1FvvkDuQhU+QsCXOyLJrJ5Xvz8djqSO0YydSbp5DcHLruuRm2bmD8jf+3dht/pZbiRos3eZFy7AdP0bZKy/iMZmIHDiYlBtvRh8aiurx0HBgH3WbNlK/Zzd4PBAQQESv3kSPGElErz5tpi/RuVLdbspeeZGGvblE9OlL2ty75e9K+AwJW+I0nvp6Kj/6AMv3W9EFBZFw+XRixk9sFatZZyLd5kVLM69fR+VH76N6PCRMn0HsRZPOeLnQU19P3bYt1G3aiKOoEICASAOGocOIHjGSkMysli7dZ6keDydff5X6nTsI79GTtLvvlb1vPqjaVkugPoDokCitS2lxErbEKfV7dlPx3jt46uoI7dCBlJvmEJyapnVZze60bvOTLyN+2hWyV0Z4neJyYVzwIeZ1a9FHRJB629yzvnztKC7CvGkjlq1b8NQ3fu8LyWpH1IiRRA0ZRkBkZHOW7tNURaH8rTewbNtCWE5X0u+9H31IiNZliZ+oc1r4y9anUVWY2+cmOsW017qkFiVhS+BpaKBywYdYtmxGFxhI/NQriL3o4la7mnUmTmMlpc8929htftRokq+VbvPCe9ymWspefRn7saOEZGaSdue959UyRXW7adiXi3nTRhr25oKioAsMJKJPX6JGjCSiR6829XWrKgoV771L3cb1hHbsRMb9D6IPDdW6LHEGH+Z9yuaT2wEI0gcyp+d19EzopnFVLUfCVhtXv3cPFe+9i8dkIiS7PSk3zyEkLV3rsjThNpsbu80XFRLRrz+pt94h3eZFk9mOHKHs3y/hMZsxDBlK8vU3eWXlxW02Y9m2BfOmjThLSwAIiI4mauhwokaMIiStda9Kq6pK5UcfYF6zipB22WT89mECwsO1LkucQVFdCfN2vEhqRDJTOl7MW/s/xKN6uKHbVQxM6ad1eS1CwlYb5bE2YPxkAXWbNkBAAPFTphF38SVt6l3xmXhsNspefgHboTzpNi+aRFVVzGvXULngQ1BVEq+8ipgJE71+iVpVVRyFBZg3bcCybSuK1QpAaIcORI0YhWHQ4Fb3NayqKlWffULtsu8ITs8g86HftelLqb5MVVX+uetVjpsLuKfvrXSN68xR0wn+vfcd7G4HM7tMZXTGcK3LbHYSttqghv37qJj/Du7aGkKy2jWuZmVkal2Wz2jsNv869Tu2S7d5cV4Ul5PKD9+nbuMGAiINpN5xJ+Fdm/+SieJy0rBnD+ZNG7Ae2A+qii4oiMh+A4gaMZLwbt1bxfaAqsVfUvP1YoJTUsl46HfSGsOH7ajYwzsHPqJPYk9u63X9qT8vsZTxUu6bWJz1XNr+Ii7OHtuq98pK2GpDPDYbxoUfU7dhfeNq1qVTiJs0WW6PPgNVUaj8+EPMa1YRmJBAxm8elG7z4qy4aqope+UlHAUnCGmXTdqd9xAUHy4aFGMAACAASURBVN/yddTWYtmyCfOmjbgqygEIjIsjatgIooaPJDg5ucVr8oaapd9Q9cVnBCUmkvHw7wmKlTdCvsrpcfLE1qepd9bzx6EPkhB2+uug0mrkxT1vUmOvZWzmKC7vNBm9zv/fDJyJhK02ouHgASrefQt3TQ0hmZkk3zSH0Kx2Wpfl01RVpeabr6he/CUBBgPp9z1AaHbbuoNGnBvr4XxOvvoyHksdUcNHkHTtDZrv+1NVFfuxo9Rt3ojl+20odjsAYZ27NF5mHDgQfWiYpjWerdoVyzB+8jGBcfFkPvIoQfEJWpckfsGS48tZWrCSie0uZGrHSWd8jMlh5sU9b1LeUMHQlIHM6jqdAH3r284iYauVU+w2jJ8uxLxuDej1ja0NJl8mq1nnwLRuDZUfvIcuOIS0u+4honsPrUsSPkZVVUyrVmL8dAEASVddQ/SF43zusojicFC/ayd1mzdizTsIgC4kBMOAgUQNH0lYlxyfvcxoWreGyvfnExAdQ+bDj/rtylxbUWOv5S9bnyY8MIw/DX2I0MCfv0u03tXAK7lvU1hXTJ+EHtzUYxZBAUEtWG3zk7DVilkP5VH+7lu4q6oITs8g5eY5hLbL1rosv/TjbvOpt9yGYfAQrUsSPkJxOql4/10sWzYTYIgide5dhHfJ0bqsX+WqrqJu8ybqNm3EVWUEICghsbF31/ARPrVqZN60kYp33iTAYCDjoUdb/Z2WrcHb+z9kZ2Uu13e7iiGpA3718Xa3ndf3vUd+7VG6xHTk9t43/GJA8zcStlohxW7H+PmnmNesalzNmjSZuEunoA9qXe8UWpr1UB5lL7+AYreTeM1sYseO17okoTFXdRVlL7+Io6iQ0PYdSJ17N0FxcVqXdU5URcF25DB1mzZg2bEd1ekEnY7wrt2IGj6SyP4DNG0Savl+Gyff+Df6sHAyH3pEOuf7gaOmEzy361XaRWXy4IC7znoflktx886Bj8g17ifLkMFdfW4hMrh13EkrYauVsR7Op+KdN3EZjQSnpZFy0xxC23fQuqxWw15USOm/nsVTV0fcpZcRP1W6zbdV1ryDlL32Ckp9PVGjRpM06zq/f0Oj2G1YduygbtMGbEcOA6APDSVy0GCiR4witGOnFv16r9+9i7JXX0IfEkLGbx+WPZN+QFEV5m1/geL6Mh4ccBfto89tb7BH8fBR/udsPbmDlPAk7u47h9jQmGaqtuVI2GolFIeDqi8/w7RqJQCxF00ifuo0mQ/WDJzGSkr/+QwuYyXRo8eQNPv6Nt+frC1RVZXa5d9R9dlC0OtJmnUdMWMu0Losr3NWVFC3ZSN1mzfhrqkBICglhejhIzEMG9HsdwE27N9L2UsvQEAAGfc/SFinzs16PuEdm8q28dGhzxmc0p8bul99XsdQVZUvjy5hVfF6YkNiuKffrSSHn/vEBV8iYasVsB05Qvk7b+KqrCAoJYWUm+YQ1rGT1mW1av/Tbf62OyTYtgGKw0HF/LexfL+NgOgY0u68u9W/1lRFwZp3kLpNG6nfvRPV5Wq8zNijJ9EjRhHRt6/Xv/ateQcpfeE5ANLve6BFepSJprO5bfx5yzyciovHhz5ETMj59z9TVZVlhWv4+vh3GIIiuavvHDIN/rtXT8KWH1OcTqoXfUHtimUAxE68iPipV2h+q3lbcVq3+S45pN19b6vr1C3+y2mspOzlF3GWFBPasRNpc+8mMMb/L2+cC4+1Acv276nbtAH78eMA6MMjMAwZQvSIUYS0y27yZUbbkSOUPPc0KAppd99HRM9e3ihdtIAvjnzDquL1XNbhYi7OHuuVY64v2cLCw4sICQjx6wHWErb8lO3Y0cbVrPJygpKTSblxDmGdZZm9pSkuF+Vvvkb9zh0EZ2SS8ZvftrkfwG1Bw4H9nHztVRRrA9EXjCXp6lltvn2Ko6yMus0bqduyCY/ZDEBwegZRw0cQNXT4eXV1t584Tsmz81BcLtLuuIvIfv29XbZoJhUNlfzt+38SGxLNH4c86NXWDTsq9jD/4AICdHq/HWAtYcvPKC4n1YsXUbvsWwBixk0g4fLpmt4t1NapitI4EHftaoISEkm//7cEJ0u3+dZAVVVqv1tK1RefoQsIIGn2dUSPGqN1WT5F9XhoOLCv8TLjnt3g8UBAABG9ehM9YiQRvfqcVTB1FBdR/PRTKDYrqbfNxTBocAtUL7zl1dy32V99iFt7XkffJO+vRh6oPsQb+97Ho3q4vttVDPKzAdYStvyI/cRxyt9+E+fJMoISE0m+aY5f9PNpC1RVpebrxVR/teiHbvO/JTQ7W+uyRBModjvl77xJ/c4dBMbGkjr3HsI6yJ29v8RTX0/dti3UbdqIo6gQgIBIA4ahw4geMfJn2zY4ykopefofeOrrSblpDlHDR7Rk2aKJDlQf4pXct+kS05F7+93WbHesHjMV8Oret/1ygLWELT/gqq3FtPw7aletAEUhZuw4EqbPlNUsH2Rau5rKD9+XbvN+zllR3rg/q6yUsC45pN5+pww7PkeO4mLMmzZg2boFT33j9+uQrHaNTVOHDCMgMhJovOuxeN6TeMwmkq67sVXe2dmaeRQPf//+n1Raq3h08G9Ij0xt1vP56wBrCVs+zH7iOLUrlmPZuR08HgITEki58Ra5M8fHWXZup/yN1xq7zc+5XS6H+Jn6vXsof+M1FJuNmHETSJxxVZvfn9UUqttNw75czJs20rA3FxQFAgKI7NuPyP4Dqfp8Ie6aGhKvnk3s+AlalyvO0eriDXx+5GtGpQ/j6pzLW+ScldYqXtrzBtX2Wi7MHMkVnS71+QHWErZ8jOrxUL97J7UrlmM/dhRo3HQaO34ChqHDpL2An7AeyqPspedRHA6SrplNjHSb93mqolCz9BuqF3+JLjCQ5OtvJGqYXM7yJrfZjGXbFsybNuIsLTn15wnTZxA3abKGlYnzYXHW88TWeYCOPw99uEW7vfvbAGu/DFufH1iqdonIISUiqcXP3Vw81gbMG9ZjWrUSd001ABG9+xAzfiLh3br7xTKpON3p3eanED/1cvl39FEem43yt9+gYfcuAuPiSbvrHpkj2oxUVcVRWEDd1s0EJyXLmxE/9fGhz9lYto0rO0/hwsyRLX5+fxpg7Zdha+Ync1WA9MhU+if1YUBSHxLD41u8Dm9wVlRgWrUC86YNqA4HuuBgokaMJHbcBIJTmvfat2h+zspKSp97GpfRSPToC0i69np0et9e7m5rnCfLGvdnlZ8krGs3Um+fS6AhSuuyhPBpxZYyntr+PMnhifx+8P2arSr5ywBrvwxb6wu2qWuPbiOv5jAe1QNAliGd/kl96J/Uh/iw5h0j0VSqqmLLP0TtimWNexhUlcDYOGLGjid69BgCIqQxZmviNpsp/dezOIqLiOw3gJTbbpfLwT6ifvcuyt96HcVuJ3bixSRMnyGjl4T4Faqq8vzu1zhiOs5dfW6he7y2d8W7FDfvHviIPT48wNovwxY/7NmyumzkVh1gV0Uuh2qPoKgKAO2jsuif3Id+ib18aoCl4nJh+X4rppXLcRQXAxDaoQOx4y8isv8A2YTbiv1vt/n7CAgP17qsNktVFKq/XkzN14vRBQeTfMPNRA0ZqnVZQviFXZV7eWv/B/RK6MYdvW/Suhzg9AHWyeFJ3ONjA6z9Omz9WL2rgdzK/eyszOVw7TFUGuvqGJ39Q/DqTXSIQYtacZvNmNetwbRmNR5LHej1GAYMJGb8xFY/V038l+JyUv7m69Tv3EFIZibp90m3eS14rA2Uv/k6DXtzCUpIJO2ue362/5MQ4nROj4u/bnsGs6OOPwx5gCQfGhDtywOsW03Y+rE6p4U9lfvZVZnLUdMJVFR06Ogc04H+yX3om9gTQ3BksxfpKC6mduVyLNu2oLrd6MPDiR59ATEXjiMo3j/3mImmURWFyg/fx7xuzQ/d5h8kODlZ67LaDEdpKWWvvICrooLwHj1JvfWOU/2ehBC/7tsTq/jmxDLGZ43h8k6+dwepqqosL1zDV8e/IzIogrv7ziHTkK51Wa0zbP2YyWFmd+U+dlXmctzc2NFYr9PTJaYjA5L70CexJxFB3rucoyoKDXtzqV25HNuhPACCkpOJHT+RqGEj0If63sY90bL+p9v8b34rd761AMvO7ZS//Saqw0HspMkkXD5dblYQ4hzU2k38ZevThASE8PiwhwnzwY3o/7GhdAuf5PvOAOtWH7Z+rMZey67Kveyq3EthXeOeqQBdAF3jOjMgqQ+9E7sTFhh2XgUpdjvmzRsxrVyBq7ICgPBu3YmZMJGInr3lm7r4H6Y1q6n8qLHbfPrd9xLerbvWJbVKqqJQvegLapZ+gy4khJSbbsEwUBrNCnGu3j3wMdsrdjO76wyGpw3Supxf5UsDrNtU2PqxKlsNuypz2VWRS3F9GQCBugC6x3dlQFJveiZ0JzTw18fhuKqrMa1egXn9OhSbDV1gIIahw4gdP5GQjMwm1ShaP8uO7ZS/+RoAKXNuwzBwMC7FzXcFq0BVGZ0xnOgQaUNwvjz19Zx8499YD+wnKCm5cX9WeobWZQnhd46bC3h25ytkGdJ5aOA9Pt+x/T98ZYB1mw1bP1ZhNbKrYi+7KnMpaygHIEgfSI/4bgxI7kPP+K4EB/z3Vn1VVbEfP0btimXU79oJikJAVBQxF44jesyFBEbJD0dx9qx5Byl7+QUUh4OwKy/nw7gTFFtKgcY3AINS+jM+azQpEbK361w4iosb92cZjUT06k3KrbcTEO5bt4ML4Q8UVeHpHS9RZCnhgf530jEmW+uSzsmPB1jP6DKVMRoMsJaw9RMnGyrYVZHLzsq9VFgrAQgOCKZXfDcGJPQkq6Aey6pV2E8cByAkM4uY8RMxDB6CPsg3O9cK32cvKqTw2afQNVjZ1jMc3cQLaBedxeri9VRaqwDoGd+NCe0uoGN0tnSi/xV132+l4t23UZ3Oxu79U6bJpXwhztOWkzv4IG8hA5P7clOPWVqXc160HmAtYevnTqCqlDWUs7Mil33Fu0jdX0qfwzYMVgUVULp3ImPSNCK79pAffKJJXIqbRUeXsDtvHZevMRNd7yF6zAUkzb4eVQf7qg6yonAdJ+oab/DIjspifNYY+iT28Jul/JaiejxUffEptcu+Qx8aSsottxLZb4DWZQnht2xuO09snYfd7eDxoQ/5VO+qc6XlAGsJW7/AWX6S2pUrqNu8EdXpxBMcyOFOkWzrqMdsCCQsMIw+iT0YkNSHnNhOPj0EU/imKlsNb+//kEJLMSkRydycNRX36+/hKC4iOD2DsC5dCM1uT2h2B4rDHKwq2cDeqgMAJIbFMy5rNENSBhLso/PAWpLHYuHk669izTtIUEoKaXfeS0hamtZlCeHXFh1dyoqitUxuP4FL2k/Qupwm+/EA6yEpA5jd9coW+dktYeunB1ZVrAcPYFq5nIZ9ewEIjI8ndtwEokaORh8WRkFdcePm+sq9mBxmACKCwumb2IsBSX3oHNtBVhzEr8o17uf9vE+xuW0MSRnAVTmXExIQjMdqpfydNxtHOXk8px6vCwkhNKsdnvRk8sLr2RJYQnUERAZHMiZjOKMzhhMZ1Db3JNmLCil7+QXc1dVE9O1Hyi23ERB2fncWCyEaVVqr+Pu2ZzEEG/jT0AdP27vsz348wLp3Qg9uboEB1hK2fqA4nVi2bqF25XKcZY2bk0M7dSZ2wkQi+/Y/47w0RVU4bi48FbwsznoADEGR9EvqRf+kPnSMyZbgJU7jVtwsPvYtq4s3EKQP4qou0xh2htuoFZcTR3EJjoLj2AsKsBecwHmyDH70mvOEBnMyVs/JWD1VCaFk9RjEyG4T/XYw+/mo27KZivfeQXW7iZ8yjbjJl8n+LCG84LW989lbdYCbe8xmQHIfrcvxqpYeYN3mw5bbZMK0dhXmtWvx1FsgIADDwMHEjp9AaPsOZ30cRVU4ajrOzsq97KncR72rAYDo4Cj6J/Wmf3If2kdlyf6uNq7aVsvbBz6koK6I5PAk5vS8lrTIlLN+vmK3Yy8qxFFwAnvBCewFBaf6uv1HQ6gee2ocSV16k5zTh9D27Qk0tL47ZFW3G+Nnn2BauQJ9WBgpc24nsk9frcsSolXIqznMS3vepGN0e+7vf0er/NnVkgOs22zYshcWNI7S+X4beDzoIyKIGXMh0ReOIyg2tknH9igeDpuOsasilz3G/VjdNgBiQ2Lon9ybAUl9yDJktMovXvHz9lUd5L2Dn2B12xiU3I+rc644q15uv8bT0IC94AS2guNU5OfiKiwivMF12mMC4+N/2PvV+F9Iu2y/HoTtrqvj5L9fxnY4n+C0NNLuvJfglLMPrUKIn+dRPPzf9n9R0VDJI4Pu9YlxN82lpQZYt6mwpSoK9Xt2Y1q5HNvhfACCU9OIGT+RqKHD0Ic0/QffT7kVN4dqjrCrci+5xgPYPXYAEkLj6J/ch/5JfciITJXg1Yp5FA+Lj3/LqqL1BOkDmdFlKsNTBzfbv7mqqhwq2k3urhW4CgtJqnGTWuMh1O457XFBySk/hK9sQrM7EJKV1SyvAW+znzhO2Ssv4a6tIXLAQFJuugV9qOzPEsJb1pZs4tPDixmRNphZXa/Uupxm1xIDrNtE2PLYbNRtXI9p1UpcVUYAwnv0JHbCRYR379Fi+ztcipu86nx2Vuayr+ogDo8TgKTwBAYkNQavc7mkJHxfrd3EW/s/5ERdIUnhCczpeR3pkaktdv5iSxmritaxs2IPEQ1u2tcF0d+RSFK1E1dREYrN9t8H63QEp6UT2r79qTsgQzIy0AUGtli9v8a8aQOV789H9XhIuHw6sZMmyxsVIbyo3tXAE1vmoagqfx72MIbgtjGovbkHWLfqsOUyGqldtYK6jetR7HZ0QUFEDRtBzPgJhKRpuyzq9Lg4UH2InZW57K/Kw6U0XvZJjUimf1LjpcbkiCRNaxRNs78qj/cOfkKD28qApD7M6jq9WTdg/pJqWy1rSjawqex7nB4noQGhjEobwsiQLgSXGU9twHcUFaI6naeepwsMJDgjszGAtWtPaPv2BKemtfgGdNXtpvKTjzCvWY0+PILU224nomfvFq1BiLbgk/xFrC/dzBWdLmVc1mity2lxzTXAutWFLVVVsR05jGnFcur37AJVJSA6hpix44gZfQEBBkMLl/rr7G4H+6vz2FWRy4GafNyKG4D0yFQGJPVhQHIfEsLazt1l/s6jePj6+DJWFK0lUB/IlZ2nMDJtiE+swFhdVjaUbmVNyUYsznoCdAEMSu7HuKzRpEWmoHo8OMvKsBeewH6icRO+o6T49BYUwcGEtssm5NQesGyCkpKb7fNzm02Uvfoy9qNHCE7PIO2uewlOkjciQnhbaf1Jnvz+XySGx/PY4AcI1PvOqnZL2lmxh3e9PMDaJ8JWTk7OeOAKoBJQ8/Pzn/iVp/xP2FLdbizbv6d25XIchQUAhLTLJnbCRAwDB/vUpZBfYnPb2Vd1kJ0VueTVHMajNv6QyzJkMDxtEMNTB0vzVB9Wazfx9oGPOG4uIDEsnlt6XuuTm0tdHhffV+xiVdF6KqyNl9Z7xndlfNYYOsV0OC04KS4XzpLiU3c/2gtONLZH+dFrXx8eTmi79oRkZzcGsPbtCYyNa3IAsx07StmrL+ExmTAMHkLyDTf7xb4yIfyNqqq8sOcNDtceZW7vm7wSMPyZtwdYax62cnJywoG9QI/8/HxHTk7O58Ar+fn5q37haafClsdiwbRuDaY1q/GYTaDTEdl/ALHjJxLaqbNPrCacL6vLSq7xADsrc8mvPYqiKiSGxTOl4yT6Jfby68+tNTpQfYj5BxfQ4LLSP6k3s7peSZhGlw3PlqIq7K/KY2XROo6ZCwBoZ8hkfLsx9E3s+bM94hS7HUdx0anVL3vhCVwVp7egCIiK+u/djz9swj+XIe2m9Wup/PB9UBQSrpxJ7MSL5WteiGaSa9zP6/veo3t8Dnf1uUXrcnyCNwdY+0LYGgf8Pj8/f9wPv38AyMjPz3/gF56mluw5hGnVcuq2bEZ1udCHhRE9cjQxY8cTlOjduwh8gdlh4buCVWws24qiKrSLymRax0voEttR69LaPI/i4ZsTy1leuIZAXQDTO09hVPpQvwsGx82FrCxax17jAVRUEsLiGZc5iqGpA8+qc7SnoQF7YcGPeoCdwF1Tc9pjAuPif9j/lU1o+w6EtGtHQPjpfW0Ulwvjxx9iXr8WfWQkqbfNJaJ7D69+rkKI/3J5XPxt27PUOEw8NvgBUmS/8CmnD7CeyMXZ487re7svhK1rgKvy8/On/fD7OcAF+fn51/7ccw48/hfVtCcXgNCUZFIvnUzSuLEEhrf+279PWipZsO8rthTvBKBfak9m955GVozvXapqC2qsJp7f+hZ5xqMkRyRw//Bb6RCXpXVZTVJmqeCb/FWsO7EFl+LGEBLJRZ3GcHGnMUSFntueR6fJRP3RY9QfOdr439GjuMx1pz0mNC2VyE6dMHTuRHhWJkUfLcCSf5iI9u3p+ujDhCbLN34hmtOivGV8tHcRk7uM44Z+rb/Vw7kqt1Ty13UvYGyo5pIuY7m+7/TzmQyjedg655WtTVOnq2FdcoidcBERffq2ydEchXXFfHl0CUdMx9GhY3BKfy7tMJG40KY1ZBVnL6/6MO8e/Jh6VwN9E3txbbcrCQtsPYG/zmlhXclm1pdsxuq2EaQPYljqQMZmjj7vcUCqquKuqTm18vWfVbDTWlAAhqHDSL7uRtmfJUQzMznMPLH1aYL1QTw+9GHCg1rP9zBvauoAa19Y2TrnPVsNhUWqNVxChaqqHKzJZ9HRpZQ1lBOoD2RMxnAubjeW8CD/7Q7u6xRVYcmJFSwrWI1ep+eKzpcyJn243102PFsOj5MtZdtZVbyeGnstOnT0TezJ+HZjyI5q+iqeqii4jJWN4auwkJCMTAzDWu/fpxC+5L2Dn7CtfCfX5FzByPShWpfj05oywFrzsAWQk5MzAbgSMAKu87kbsS1TVIXt5bv5+vgyah0mwgLDuKjdhYzJGEFwM08yb2vMjjreOfARR0zHiQ+N45aes2kXlal1WS3Co3jYbdzHysK1FNeXAdA5pgPjs8bQPT5HBq4L4WcK6op4esdLpEem8rtB98lr+CzY3Q5e3zf/1ADr23rfcFY3QvlE2DoPErbOwOVxsa50M8sKVmN124gNiWFyh4kMSekvLyIvOFRzhHcPfIzFVU+fxJ5c23VGm1xyV1WV/NqjrCxaR17NYQBSIpIZnzmagSn9CGqjvXmE8CeKqvDszlcoqCviN/1up7PcbHXWzmeAtYStVsjqsrK8cC1rSjbiVtykRaQwteMkesR3lUsz50FRFb49sZJvC1ah1+m5vNNkLsgYIX+XNDZBXFm0jh0Ve1BUhejgKC7MHMnI9CGtav+aEK3N9+W7mH9wAf2SejOn58/ejyZ+hkfx8HH+F2w5uf2sBlhL2GrFau0mvjmxnG0nd6Ki0jmmA1M7XkL7aP++W64lmR0W3j34MYdrjxIXGsstPWd7ZZ9Sa1NrN7G6eAObyrbh8DgJDQhhRPoQLswY+YvfgIQQLc/udvCXrU9jdVv545AHiQ+L07okv3QuA6wlbLUBZfXlLD72Lfur8wDol9iLKR0vJsnLU81bm8O1R3nnwMfUOS30SujOdd1mEiE3Hvwiq8vGxrKtrCneSJ3Tgl6nPzUOqCUHcAshft7Xx77ju8LVTMoex6UdLtK6HL92tgOsJWy1IUdqj7Ho2LcU1BWh1+kZkTaES9qPJyrY9+ZFaklRFZYVrGbJiRXodDqmdbyEsZmj5LLhOXApbraX72ZV0TrKrZUAdI/PYULWGDrHdJS/SyE0UmWr4a/bniEyKII/DX2IkLNoWCx+3a8NsJaw1caoqsoe436+OvYtlbYqggOCGZc5mvFZown18dEyLcHirOfdAx9zqPYIsSEx3NxzNh2i22ldlt9SVIUD1YdYUbiOY+YTAGQZ0hmfNYa+ib1kzqcQLeyNfe+zx7iPG7tf0+R5f+J0vzTAWsKWHygx1vPO0jyMJjvxUaHER4ee9mtCdOP/R4QGnvWKgUfxsKnse5YWrMDirMcQFMmk9uMZmTakzf4APFJ7jHcOfITZaaFnfFeu634VkUG/fIeJOHsnzEWsLFpHrnE/KirxoXGMzRrFsNRB8u5aiBZwuPYoz+9+nQ7R7Xig/52ywtwMDlTn88a+9/CoHq7rNpPBKf0BCVs+TVVV1u4uZcHqo7jcCkkxYdTWO3C5lTM+PiQo4CdBLIT46FASosKIjw4lOjIY/U9eXHa3g9XF61lZtA6Hx0liWDyXdbiY/km928wLUVEVlheu5Zvjy9DpdEzpcDHjskZLu4xmUmk1sqp4A9tO7sCluIkIDGd0xjDGZIzAEBypdXlCtEoexcNTO16gtP4kDw+8p830B9TCfwZY29x2ZnaZxpiM4RK2fFW9zcU7S/PYfaSKiNBAbr6kG/26JKKqKhari+o6O9VmO1Vm+6n//8+vVof7jMcM0OuIiwr5n9WxhKhQQsPdbKvZxKaT2xoHXRsymdZpEl1iO7XwZ96yLM565h9cQF7NYWJCorm5x2w6xmRrXVabYHHWN44DKt1Mg8tKkD6QIakDGZzcn9SIJJmCIIQXbSjdwoL8LxmaOpDrus3UupxW76cDrK8ffLmELV+TX1TL618fpNbioGtWDHMu7U5c1Nnvp7I53I1B7Cch7D+/mhucZ3yeDoiKdaJPP4I9vBiAlMB2jEy8kC4JWcRHhxIa3HoaVh41neDt/R9idtbRPS6HG7pf/auN6YT3OTxOtpzczuqiDVTba079uSEokuSIRJLDk0gJTyQ5Ionk8CTiQmNk1VGIc2B1Wfnz1nm4FTePD32E6BC5KaolVFqreGnPG1Tba1l41asStnyFR1H4elMBX28uQIeOqaPaM3loO/R6717Oc7k91NQ52iWQewAAIABJREFU/hvGfhLIauocqOEmgjLzCYiqQVXBU52Gu6Qz4XrD/6yKxf9/e/cdGNV1JX78O+q9N0AFiXKR6IhmG1NMsQ027onjjntMkvXGTjbZTdySbSlO+a1bHLAdh8Sx4wIYbHqzMU10SVyBhCrqvY40M+/3xwwdbEDlaaTz+Qdp9GbmDNLMO+/ec88NPT1SFuTv3eunHx2Gg/UFW1h5fA0ANydfz5ykGXICN5ndYedQVRa59fmUt1RS3lxBdVstBmd/xnh7eBETEE1swNmJWExAtNR+CXEB/8xZwabiL7h1yHzmJs00O5x+pc5az58O/YVf3/jvkmz1BlX1rfxpZRbHiuuJDPHjiYUjGRofakosDodBXZOVyrpWDlcdYXf9VhqNaiyGB951Q2gtTKLdeuERriupG+tJTe3N/CX7H2RWHyHUJ4SHR9173hJd0Xt02DuoaK06lXyVtVSc+rrd0XHe8eG+YcQFxpxOxFwjYyE+wb3+IkCI7lDaXM5/7fodEX7h/GzKM7KdlkmkZqsX2HOkgrc/O0KL1cbEETE8dIMiwK/3bCB9oY2uZwyYjgoYR0OjvcvqxiJD/YgI8cPLs3tGmHLr8lmauYw6az2pEcN5MO1uKch2Uw7DQb21wZl8NVdS3lJBmSsJq29vOO94P08/YgOjiQtwJWKBzhGxKP9IvOTkI/oowzB45cASsmtyeGL0g4yJHml2SP2WJFsmsnbYeW/DUbbsP4GPlwf3zB3OtWMG9Nor8HM3ug7zDeWm5HlMGZB+3hRcZ+rGQoN8zkrERiSGMyo54or/XxyGgw2FW1mR9zmGYXBTyjzmJc2SacM+qtXWRkVL5RmjYc5krKKlCrthP+tYD4sHUf4RrunIsxMxKdAX7u5QVRavH3ybEeHD+N64R3vtuaU/kGTLJMUVTby+IpMTVc3ERwfx5C0jGRjlHsXZLR2trC3YxObiL+hw2BgQGMstQ25kVGTqJb+Zv6lurLbRit1x+m9r6KBQ7piRgkoMv6xYmzta+EvWPzhcnU2ITzCLRt7DcNndvl+yO+xUt9VSfsZU5MnRsGZby3nHS4G+cGc2h41f7vwt1W21/HTS0wwMijM7pH5Nkq0eZhgGm/aV8N6GY9jsDmanx/OtWUPw9nK/RqK1bXWsOr6OHaV7MDAYGpbMrUMWdMlG1yfrxsprW1m/p4h9R6sAGJUcwe0zUhgcF/KNj3G8voAlh5dRa61DhQ/loZHfka2JxAU1tTe76sHOnpasbq25aIF+TEC0MwkLiDmVlEmBvugt1hdu4eNjq5gRfw3fGn6L2eH0e5Js9aAze2cF+Xvz8PxUxg2LMjusTjvRVMaKvM84VOXc6Hqca6Pri+1+fiXyTjTw0dZcsvJrAUhX0dx2bcoFRwMNw2Bj0TY+yV2NYRjMT57DDYNny2iEuGwd9g4qW6vPqg07mYi128+fCg/3DTtrKvJkIhbqEyJTOKLHNLQ38uJXv8LT4snzV/2YQJkSN50kWz3k3N5Zj908kvBgX7PD6lJHa/NYnrua466Nrq8eOJn5g+d2aU+X7PwaPtyaR96JBiwWuHpkHLdMSyYqzB9w9pN5N/sDDlZlEuwTxKK0e1ARfbsxq+h5hmFQZ62nvKXynESskjpr/XnH+3n6njUCdnJaMloK9EU3WJb9AdtLd/Pt4bcyPf5qs8MRSLLV7ewOByu+yOfT7flYLN3XO6u3MAyDA5WHWZ73GRUt3bPRtWEY7D9WxUdb8yipbMbTw8LMcYMYO8aTf+S9T01bLcPDhvDQyHukeZ/ocW22NmdN2DkF+pUtVdguVKDvF0FsYDSTYseTHjvOpKhFX1HYUMyv9vw/BgTG8pNJ/9Jr9rp1GAZ5JQ34+niSENP/VoFLstWNqupb+dOKLI6VmN87q6fZHXa2l+5i1XHnRtdB3oGnNrruqit5h8NgV3Y5H2/Lo8b3CN4JGouHwZz4Wdwy7HqZNhS9isNwUN1a65qGPHs0rKmjGYBFad9hYtx4kyMV7sowDF7e+xp59fl8f9xjjIgYZno8heVN7MwqZ2d2ObWNVgCGxYcyZ2ICE4ZH4enRPz6nJdnqJmf2zpo0IoYHe1nvrJ7SZrOyqWgb6wo3Y7W3E+UfycKU6xkfM6ZLkqGWjlbezX6fg1WZYPPBemwMvtY4bpySyJyJ8X1qeyHRdxU2FvOHvX+iw9HBU2MfNv0kKdzTnvL9vJX5N8ZGj+Lx0Q+YFkdFbQs7ssrZmVVOabVzpW+ArxcTVDT1Te0cyqsGICLEl+smxDN97ECC/Pv2+VGSrS52Vu8sbw/umdO7e2f1lMb2Jj7L38AXJTuwG3YSg+O5dcj8TtVTFTQUseTwMqrbahgalsx9w+8mI7OR1TsKaGrtICTAmwVXD2bmuEF4e/WPqyfhvnJqc3ll/5/x9PDkXyd8l4TgQWaHJNxIu72dF3f8mqb2Jn4+9Vmi/CN79Pnrm6zsOlLBzqxy8k44Gwt7e3kwdmgUU9NiGZ0SeepzuLS6mQ0ZxXx5qAxrhx0fLw+mjoxjzsR44qP75hSjJFtdqKiiideXH6a0uoWEmCCeWOg+vbN6SmVLNSvzPiej4gAAaRGKW4fOZ1DQgEt+DMMw2FKynY+PforNsHND0nXMT557qjah1Wpjza5C1uwuwtpuJzLEl4XTkrl6VFy/GbIW7mlvxUGWHl5GkE8gz6Yv7vETpnBfq/LWsjp/PfOSZnHLkBt75Dlb2mzszalkZ1YZWQW1GAZYLDBycART0mKZMDwaf9+Lzy60tNn44uAJ1mcUU1XfBkBqUjhzJsYzdkhUn6ptlmSrCxiGwca9Jfxjo7N31pz0eO5y095ZPaWgoYhPcj8jp/YYFixMihvPTcnXE+n/9U1LW22tLDvyIfsqDhLkHciDaXeTFqkueGxjSzurdxSwIaMEm91BXEQAt01PIV1Fm7o/oxBfZ3PRl3xwdDnR/pE8k75YtpQS36imrZaXdvyaAC9/npv6oy5bjHQhHTY7B3Nr2JlVxv5j1djsDgCGDAxh6sg4Jo6IITTw8vrNORwGB45VsT6jmOwCZ3uf6DA/Zk+IZ9qYgQT4uX85iCRbndTU2sHSVdnsP9a3emf1BMMwyK7J4ZPc1ZQ0leJl8WRG/DXMGzyLIO/zRwQLG4tZcngZVa3VDAkdzKKR9xDuF/aNz1PT0Man2/PZeqAUh2GQGBvE7dOHMDrlyrcAEqI7Lc/9jLUFm0gKTuAH4x/Hz6tvtYkRXWvp4WVkVBzggdRvM2VAepc/vsNhcKSwlh1Z5WToSlpde98OiAxg6sg4pqTFEuNqv9NZxRVNrM8o4qvMcjpsDnx9PJk2agCzJ8YTF+G+/cIk2eqEIwW1vPlp3+6d1RMchoM95ftZmbeGmrZa/L38mJc0i5nx0/Dx9MYwDLaV7ODDoyuwGXbmJc3ipuR5l72kuby2heXbjrMzqxwDGB4fyu0zhjA84ZsTNiF6kmEYvJv9PjvLMkiLUDw55qFes4Rf9C7H6o7zu72vkRSSwLPpi7tsFbZhGOSXNZ5aSVjf5GziGxHiy5TUWKakxZIQE9RtF6xNrR1s2V/Cxr0lp1Yxjk6JZM7EeEYmR7jd7IQkW1fA7nCw/It8Vrl6Z916bTLz+3DvrJ7SYe9ga8lXrMnfSLOthTDfUOYPnoOuPUZGxQECvQN4IPXbjIpK7dTzFFU08fHWPPYfc24BNDolktunp5AUJz25RO9hd9h5/dDbZFVrpsSlc3/qt2QkVpzFYTj41e4/UtR0gmfTF5McmtTpxyyraWFHZhk7s8opr20FINDPi0kjYpiSFsuwhLAeTXRsdgf7jlaxbk8Rx4qdDYPjIgKYMzGeq0fFuc2Kc0m2LtOZvbOiQp29s4YM6h+9s3pKS0cr6wo3s6loGx0O53B1SmgSD4+895KmDS/VsZJ6PtqSy5HCOgAmjojhtmuTGRApixpE72C1t/OHvW9Q0FjUo4XPwj18eWInfzvyIZPjJvBg2t1X/Di1jVZ2ZZezI6ucgjLnudXH24Pxw6KZkhbLqOQIvDzNX1yUX9bA+j3F7Moux2Y38Pf14toxA5idHk90F01jdhdJti7DblfvrFarjcmpMTxw/Yg+UbjXW9W21bGucDOBXgHcMHh2t0yjGIZBVkEtH23J5XhpIxYLXDN6ALdck0xkaPcVmQpxqRrbm3g541UqWqu4c9hCZiVMMzsk0Qu02lp54atf0e7o4PmpPyLM9/Iu+pvbOsjQlezMKudIQS0G4GGxMColgqlpsYwbFtVrR43qm9vZvK+ETftKaGhuxwKMGxbFnIkJjEgM65UjwJJsXQJru52/b8hh64FSfLw9uHfOcKZJ76w+xTAM9h11bgF0oqoZL08LM8cPYsFVgy97ZY0QXa2qtYbfZrxCY3sTi0beQ3rsWLNDEib76OinbCjays0pN3DD4Osu6T7tHXYO5FazI7OMQ3nV2OzO8/ew+FCmpsWSPiKGkAD3+byz2R3szq5g3Z4i8l0jcvHRgcyZmMDUtFh8vHtPnaMkW9+gsLyRN1Zknuqd9eQtI2WaqQ9zOAx2ZJXxybbjVNW34evtydxJ8dwwObFf7gAgeo+ixhJ+v/d1bA4bi8c9wvBw2WC9vypvruCXu14m3DeUn095Fm/Pi3822R0Osgtq2ZlZTkZOJW3tzv0546ODmDoylsmpMUSF9u4puG9iGAa5JxpYv6eIPUcqcRgGQf7ezBg3kFnjBxERYv4shSRbF3uCc3tnTYznrpnSO6u/sNkdbDtwghXb86lvaifA14sbpyYyJz0BXx/5GxDmOFJzlFcPLMXbw5t/nfAk8cEDzQ5JmOC1A0s5XH2Ex0bdz7iY0ef93DAM8k40sCOrnN3Z5TS0dAAQGeLH1JHOlYR9tVN7TUMbm/aVsGX/CZpaO/CwWEhX0cydmMCQQSGmzUhJsnUBjS3tvLX6yOneWQtSGTdUemf1R9YOOxszilm9o4DmNhuhgT7cdPVgZowb2CsKRkX/c3L/u1CfYJ5JX0ykf4TZIYkelFl9hFcPLGV42BB+MP7xs5KHE1XN7MhyriSsrHN2ZA/y92ZyagxT0+JMTTZ6WnuHnR1Z5azfU0RxpXOj96S4YOZOjGfSiNge38JNkq1zZBfU8ubKTOqa2klNCufRm9Kkd5agpc25BdDa3UVYO+xEhfpxy7RkrhoZJy0/RI/bWLSND4+uJDYgmh+mP3XBJsDu4PDxasqqW4gI8SMixJfwYD+CA7zdrodST7E77PznrpepaKnip5OfZlDQAGoa2tiZXc7OzHIKK5oA8PX2ZMLwKKaOjCM1KbxfXxgahoEurGPdniL2H63CAEICfZjpmmIMDeqZ87skWy42u4MVXx5n1fYCLBYLt01P5sYp0jtLnK2h2bkF0Ma9zi2ABkQGcPv0FCYMj+43V4yid/j42CrWF24hOSSRH4x/HB9P9ylsBsg70cB/vZuB45xziJenhfBgXyKC/QgPcf4b4fo3PNiXiBBfgvy9++X77WSSPTV2MgntV7Ejq5ycImfrGk8PC6NTIpk6MpaxQ6Pw7UXF4b1FZV0rG/cWs/VAKa1WG54eFianxjBnYgLJA0K69bkl2QKq6lp5Y2UmuSUN0jtLXJKahjZWfHmcLw6W4TAMkuKCuWNGCiMHyxZAomc4DAd/yXqf3eV7GRWZyuOjH3CbLvPWdjsvvLWL8tpWvn3dUAwDahrbqG2wUtPYRk2jlYamdi52dvHx8iA82NeVfJ2bjDm/D/D16lPvxermen6x6zfY7QZtB6/F3u6NBVCJYUxJiyVdxRDkL4t4LkVbu42vDpexPqOY0uoWAIYOCmXOxHgmDI/ulpHAfp9s7cou553PtfTOElekrKaFT7blsSu7AgCVEMYdM4YwNF6SddH9bA4brx98m+yaHK4eMIl7RtzpFgnGu2s0m/aVMG9SAnfPHnbBY2x2B3WNVmoaracTsTOSsdqGtlOF3xfi6+15aiTszFGxiBA/IoKdU5a9/bPeZneQlV/Djqxy9rduwhJVSHvBCAYxiqlpcUxOjekVK+3clcMwyDpew/qMYg7mVgMQHuzLdRMGMX3sQIK7sA1Gv022zuudNXc400ZL7yxxZQrLG/loa96pN+yYIc4tgBJjZQsg0b3abG38Yd8bFDaWcOPg2dyUcr3ZIX2tg7nV/P6DAwyKDuS5Byd2aoV3h81ObVM7tQ1t5yRiVmoanF83tV48IfPz8Twj+TqdiEWEnE7Oerqxp8MwOFZcz86scnYfqaCptQNLQAN+I7cTaAnn+6MXkxDdvVNe/VFZTQsb9hTzxeFSrO12vL08mJoWy5yJCSTEdH7lZr9Mts7snZUYE8QT0jtLdJGjxXV8uCXvVB3F5NQYbr02xa13qxe9X0N7I7/NeJWq1mq+Pfw2psdfZXZIF9TY0s5zS3bR1NrBzx+c2CMXI+0ddmobTydfJ0fFaly31TZaaW6zXfT+Ab5e59SOOUfFTo6ShQf7dkl9VHFFEzuyytmZVU51g3MlYUigD5NGRFMYtI7i1kIWj32EtEjV6ecSF9fSZuOLQ6VsyCg6taJzRGIYcyYmMG5o1BXXcferZMswDDZkFPP+pmPY7AZzJyZw58whPb4EVPRthmGQmV/Dh1vyKChrxMNiYdqYOBZekyxD/qLbVLZU89uMV2jqaObRUfddsP+SmQzD4JWPD7M3p5K7Zg7hxqmd3zS5q7S121wJmfXs2rEGq/P2xjZarfaL3j/Qz+uCo2InE7TwYN8LjuBV1bWy07UnYYmrPYGfjyfpKpqpaXGMSArjQNVhlhz+K6OjUnlyzKJu+z8QZ3M4DA7kVrF+TzHZBbUARIX6cd2EeKaPHXDZTa77TbLV2NLO0lXZHMitJsjfm0cWpDJWemeJbmQYBhm6ko+35VFa3YKXpwfXTRjE/KuS3GpLDOE+ChuK+d2+13EYDr4/7jGGhiWbHdIpXxwsZenqbIYnhPHj74x3u5XerVbbqZGwmjNGyk6Pklmxdlw8IQsO8D5VOxYe4ktReRPHSuoB5wrMMUOimJoWy5ghkae2mWm3d/CLnb+h3trAz6b8kJiA6B55reJsxZVNrN9TzFeZZXTYHPh6e3L16DjmpMdf8qxYv0i2svNr+NOnWdS7emc9dnMaYT3UW0MIh8Pgq0znFkDVDW34+ngyb2IC109O7PUFusL9ZFfn8OrBpfh6+vLDCd9lYFCc2SFRWdfK80t3AfDSw5OJCnPv7WEuxDAMV0J2elTs3CnLmkYrHTYHABZgRFK4c09CFX3BkZLPjm/g0+NrmJM4g9uGLujhVyTO1dTawdYDJ9i4t5iaBisAo5IjmDMxgVEpEV/bH65PJ1s2u4PlXxxn9Vdn9M6amiQN84QpOmwOth44wcrt+TQ0txPo58X8q5K4bkK89MQRXWpX2V7eyXqPMN9Qnk1fTLhfmGmxOBwGv/rbXnKK63lkQSrXjB5gWixmMwyD5jbnCFlooM/XNtSsbavjpR2/xtfTl+ev+jH+XlKC0FvYHQ725VSxbk8RR4udo5OxEQHMSY/n6lFx+PuefxHdZ5OtyrpW/rQik9wTrt5Zt4xkyEBZji/MZ223sz6jiM92FNJitREa5MPCqwdz7VjZAkh0nfWFW/j42CriAmL4YfpTBHqbs0hj9Y4C/rk5l3QVzVO3jpIV35fo7cy/s7t8H/eOuIurB04yOxxxEQVljazfU8TO7HJsdgN/X0+mjR7I7InxxJwxgtsnky1n76wjtFrtTEmL5f55SqZrRK/T0tbB564tgNo7HESH+XHrtBSmpMW6XT2L6H0Mw+CjY5+ysWgbKaGD+f64x/Dx7Nmml4XljfzinT0E+Xvz0iOTu7RvUV+WV5/PbzNeJTF4ED+a+H08LHIR1tvVN7ezZV8Jm/aVUN/cjgUYOzSKORPjSU0KJyYmpO8kW9Z2O8vW5/DFwVJ8vT25d+5wrhkdJ1dSolerb25n1fZ8Nu8vwWY3GBQVyG3TUxg/LEr+dkWnOAwHb2f+nYyKA4yJGsmjo+7rsS7zHTY7L729h5KqZv71W2MZnRLZI8/r7hyGg1/v+T8KG4v54YSnGBI22OyQxGWw2R3sPlLB+j1FHC915imDogN5/Sdz+kayVVjeyOvLMymraSExNognFkrvLOFequpbWfFlPl8eKsUwYPywKBbfPlpqDEWndDhsvHZgKbr2GNcMnMJ31O09ksS/t+Eoa3cXMWvCIO6fJ72hLtVXpXv4a/b7TIwdx6KR95gdjrhChmGQe6KB9XuKyNCVfPLrhe6dbBmGwfqMYj5w9c6aNymBO2ZI7yzhvkqrm3nnc01OUR13zEhhwVWDzQ5JuLlWWxu/3/s6xU0nWJA8l/nJc7v1+bLya/jNe/uJjQjghUWTZAHIJWq1tfHijl/RZrPy/NQfmbqwQXSdptYOkhMjOpVsmZrRNLS084d/HuTv64/i7+vF03eN4e7ZwyTREm5tQGQgi28bRXiwLx9vPX6qG70QV8rfy4+nxj5CpF84q46v48uSnd32XM1tHSxZlY2nh4XHb06TROsyrMnfSGN7E/OSZkqi1Yd0xQbhpmU12fk1PL90Fwdzq0kbHM6LD09mzBBpUir6huAAH55YOBIDgzdWZH7t/m5CXIpQ32AWj3uUIO9A/q4/4mBlZrc8z7K1OdQ2Wrn5msEkD5A9/C5VRUsVm4q2Ee4bxpzEGWaHI3oZU5Ktv6zO4jfv7aeppYO7Zg7hh98eJ01KRZ8zPCGMW69NobbRytJV2Zg0ZS/6kNiAaJ4cswhvDy+WZi4jrz6/Sx9/Z5Zz65mUgSEsuKr3bMfjDj4+tgqbYee2oQvw8ZRVm+JspiRbH2w4SlSYHz+9L12alIo+bcHUJFKTwtl/rIp1e4rNDkf0AcmhiTwy6j7shoPXDrxFWXN5lzxuTUMb767R+Hh78NhNaXh6SDnHpcquyeFgVSZDQpOZEDPG7HBEL2TKu+mGqwbzwqLJpAyUIWrRt3m46l5CArz5YNMxjpc2mB2S6ANGRaVyz4g7abG18n/7l1Bnre/U4zkMgyWrsmmx2rh79jBiI8xpoOqO7A47/zy6EgsW7hq+UNq9iAsyJdlafOfYC7bDF6IvCg3y5bGFI3E4DF5ffpiWNpvZIYk+4KoBE1mYcgO11jpe2b+Elo7WK36sDXuKyS6oZeyQSGaMHdiFUfZ9207soKy5nKsHTiIheJDZ4YheSsaJhegBIwdHsODqJCrr2njn8yNSvyW6xLykWcyIv5oTzWW8cehtOuyXvxCjpKqZDzbnEuTvzUPzU2Vk5jI0dTSzKm8tfp5+3Jxyg9nhiF5Mki0hesgt05IZFh/K7iMVbNl/wuxwRB9gsVi4c9hCxkeP5ljdcd7Oeg+H4bjk+9vsDt5ckYnN7mDRjSMIDZTC7suxKm8dLbZW5ifPIdgnyOxwRC8myZYQPcTTw4MnFo4k0M+Lv60/SlFFk9khiT7Aw+LBg2l3Mywshf2Vh/ggZ8Ulj5wu/+I4hRVNXDtmAOOHR3dzpH1LSVMp20q+IiYgihnxV5sdjujlJNkSogdFhPjxyII0bHYHry8/TFu71G+JzvP29Obx0Q8yMDCOrSXbWVuw6Rvvk1NUx+odBUSF+nH37GE9EGXfYRgG/zy6EgODO4bejJeH1CCLryfJlhA9bNywKOZNSqC0uoVla3PMDkf0EQHe/iwe9wjhvmGsyPucr0r3XPTYVquNP3+aBcBjN6fJgqXLdLAqk5zaY6RFKkZFpZodjnADkmwJYYI7Zw5hcFwwXx4u48tDpWaHI/qIMN9QvjfuEQK9AvjbkX9yuCr7gsf9ff1RqurbmD81iWHxsq3MpWpsb2JV3lrezf4AD4sHdwy92eyQhJuQZEsIE3h5evDkraPw9/Xkr2tzKK1uNjsk0UfEBcby5NhFeFo8WXL4rxyvLzzr5xm6ki8OlZIUG8wt05JNitK9VLZU8w/9MT/f/t+szl+PBbh7+G3EBcaYHZpwExaTlqAblZWNZjyvEL3K7iMVvPbJYeKjg/jZA+n4yKa/ooscqsrijYPvEODtzzPpi4kNiKa+ycrPl+zC2mHnuYcmMSgq0Owwe7WChiLWFW5hf8UhDAwi/MK5LuFarhowCT8v2WKuP4mODu5UTxRJtoQw2V/WaDbvK2Hm+EE8cL0yOxzRh3x5Yid/O/IhkX7h/HDCU7y9Mp+DudV8Z84w5k5MMDu8XskwDLJqNOsLtpBTlwtAfNBA5ibOYHzMGDw95IKoP+psstWpqkillAfwGPAL4Dqt9eHOPJ4Q/dHd1w3lWHE9m/eVkJoUzqQRMjUhusY1A6fQYG3k0+Nr+dXONyjLH0va4Bhmp8ebHVqvY3PYyCg/wPrCLZxoLgNgRPgw5iTNYET4MGn2Kjqls0tQxgI7gZYuiEWIfsnH25Pv3jqSl97ew9ufZZMUF0xMmL/ZYYk+4obBsyltqCGjeg/+w/fz4Mzv4SGJwylttja+PLGLjUXbqLPW42HxYGLsOOYkzpDtd0SX6VSypbXeB6CUTH0I0RkDIgO5b95wlqzK5vVPDvPv96fj5SnrV0TnOQyD4v2DsQcW4hlRwcqiT3go5Dt4WPr331e9tYHNxV+yreQrWm1t+Hh4Myt+GrMSphHpH2F2eKKP+cZkSym1Boi9wI+e01qvuNInjo4OvtK7CtEn3XpdMMfLm9i4p4hVO4t49JZRZock+oC/rzlCfmkT0yfMpzFqGxkVB4gNjeDB8Xf1y6mxkoYyVh5Zx9aCXdgcNkJ8g1g44mbmDZ1OsK9suSO6xzcmW1rr67vjiaVAXojz3Tk9mezj1SzfmktSdCDjhkWZHZJwY3knGnhvXQ4RIb58a/ow8BzMy3tfY/XRTfg4/JmbNNPsEHtMbl0+6wo3c6jK2cw1xj+K6xKnMyUuHR9Pb9oaDNqQ85K4sM4OEEnbYCF6ET8fL568ZRS//MselqzK4sVIXhF5AAAbBElEQVSHJxMR4md2WMINWdvtvLkyE4dh8MiCNAL8vAFvFo99hN9kvMInuasJ8QlmyoB0s0PtNg7DwaGqLNYXbiGvvgCAwSGJzE2cwZjokf1+KlX0nE61flBKhQOLgWeAd4G/aa13XMJdpfWDEF9j874S/rJGMzQ+lH+7ZzyeHnJSEJfn3TWaTftKmDcp4by9D080lfHy3tew2q18d8wi0iL7Vt1th72DXWV72VC0lfKWSgBGRaYyN2kmQ0IH98vpU9E50mdLiD7IMAxeX57J7iMV3HR1ErdPH2J2SMKNHMyt5vcfHGBQdCDPPTgRb6/ze0MdqzvO/+1/E4vFg6fHP0FSiPv33WrpaGFryQ42F39BY3sTnhZPJsWNZ07iDAYEXqj0WIhLI8mWEH1US5uNF9/eRVVdGz/89jhGJssKKfHNGlvaeW7JLppaO/j5gxNJjL14rcmBysO8eehdAr0DeCZ9MTEB7lkjWNNWy6aiL/jixE7a7e34efpx7aCpzEy4hjDfULPDE32AJFtC9GHHSxv4r3czCPTz4sWHJxMaJFuEiIszDINXPj7M3pxK7po5hBunJn3jfbaVfMV7+mOi/CJ4ZuJiQnzcZ6V4SVMp6wq2kFGxH4fhINQnhOsSr+WagZPx95JedaLrSLIlRB+3dncR7204SmpSOM98exweHlJvIi7sy0OlLFmVzfCEMH78nfGX/Lfyad5aPstfT0LwIJ4e/wR+Xr13UYZhGOTU5rKucDPZNTkADAiMZXbiDCbFjsPLQ9Z9ia5n6nY9QojuN3diPEcKatl/rIpVOwq4+erBZockeqHKulaWrcvBz8eTRxekXlZSviB5LvXWBraX7uLNQ+/y3bGLel3SYnfY2V95iHWFWyhqLAFgWFgKcxJnkBapZGWh6NV617tJCHEei8XCwwtSeeGtXXyyLQ+VEMbwhDCzwxK9iMNhsOTTLNra7TyyIJWoy9zuyWKxcLe6jcaORg5VZfPX7A94IO3bvSKBsdrb+ap0NxsLt1HdVoMFC+OiRzMncQbJoYlmhyfEJZFpRCHcxNHiOv532T5Cg3x4YdEkggN8zA5J9BKrdxTwz825pKtonrp11BW3Nmi3t/PHfW9yvKGA2YnTuX3oTV0c6aVrbG9ia/F2tpRsp7mjBS8PL6YOmMjshGuJCYg2LS7RP0nNlhD9yKqv8vlwSx5jhkTyL3eOkX5BgsLyRn7xzh6C/L156ZHJnU7CmzqaeTnjVcpbKrl96E3MTpzeRZFemsqWajYWbeWr0t10OGwEegUwPf4qZsRfQ7CPbKcjzCE1W0L0IzdOTeJIQS0Hc6tZu7uI6yfLNEp/1mGz8+bKLOwOg4cXpHbJaGeQdyCLxz7KbzP+j4+OfUqoTzAT48Z3QbRfr6ChiHWFW9hfcQgDgwi/cGYnTOeqgZPw9ZRRXOHeZGRLCDdT39zOC0udfZR+el86KQNDzA5JmOS9DUdZu7uIWRMGcf+8ru0CX9JUyssZr9Hh6OCpsQ8zImLYN9/pMhmGQVaNZl3BZo7W5QGQEDSQOUkzGR89Gk+P85uxCmEGmUYUoh/Kyq/ht+/tJzLUjxcWTXLteyf6k6z8Gn7z3n5iIwJ4YdEkfL27PjHJqc3llf1/xsvDi6cnPElC8KAueVybw0ZG+QHWF27hRHMZAKkRw5mTOAMVPlSmx0WvI8mWEP3Ux1vzWLk9n4kqmu92oihauJ/mtg6eW7KLhuZ2/v3+dJIHdN/o5t6Kgyw9vIwgn0CeTf8eUf5XvpNBq62NL0/sZFPRF9RZ6/GweJAeM5bZiTNICB7YhVEL0bWkZkuIfmrhtMHoojr26Eo27yth1oR4s0MSPWTZ2hxqG63cem1ytyZaABNixtAwrJEPji7nlf1/5ofpT112oXq9tcG1nc4OWm1t+Hj6MCt+GrMSriXSP7ybIhei95CRLSHcWG2jleeX7qKt3c7PHkj/2n3wRN+wM6ucN1ZkkjIwhJ/eNwFPj57phbU89zPWFmwiKTiBH4x/HD+vb946qqy5gg2FW9hVthebYSfYO4iZCddw7aCrCPQO6IGohegaMo0oRD934FgVf/jnQWIjAnj+oYn4+ciAdV9V09DGc0t2YXM4eHHRZGIjei5hMQyDd7PfZ2dZBmmRiidHP3TRAvbcunzWFW7mUFUWADH+UcxOnM6UuHS8PaW+ULgfmUYUop8bOzSKGyYn8vmuQt5do3n0pjSp3+qDHIbBklXZtFhtPHCD6tFEC5xd5u8dcSeNHU1kVWuWHfkn96d+69TfmsNwcKgqi3UFWzjeUABAckgic5JmMiYqrVd0oxfCLJJsCdEH3D4jBV1Ux1eZ5aQmRTBtzACzQxJdbMOeYrILahk7JJIZY80pJvf08OTRUffzh71vsLMsg1DfEOYPnsOusr2sL9pCRUsVAKOjUpmTOJMhoYMl8RcCmUYUos+orGvlhbd2Y3c4eO7BSQyMCjQ7JNFFSqqaefGt3fj5ePKLR6cQGmhuk8/G9iZezniVitYqArz8abG14mnxZHLcBGYnTmdAYKyp8QnR1To7jSjjukL0EdFh/iy6cQTtHQ5eW36Y9g672SGJLmCzO3hzRSY2u4OHbhxheqIFEOwTxOJxjxLqE4LDMJibOJOXrv4J96XeJYmWEBcgI1tC9DHvrtVs2lvCjHEDefCGEWaHIzrpwy25rPqqgGljBvDw/FSzwzlLm82Kh8WCj2ynI/o4GdkSQpzl7uuGkhATxJb9J9iVXW52OKITcorqWL2jgKhQP74zu+u3y+ksPy9fSbSEuASSbAnRx3h7efLdW0fh6+3J258doaK2xeyQxBVotdr486fO1gmP3ZyGv6+sZxLCXUmyJUQfFBcRwAM3KNra7by2PJMOm8PskMRl+vuGo1TVtzF/ahLD4sPMDkcI0QmSbAnRR101Mo5powdQUNbIB5uPmR2OuAx7cyr54mApSbHB3DIt2exwhBCdJMmWEH3YvXOHMyAygPV7itmXU2l2OOIS1DdZefuzI3h7efDozWl4ecrHtBDuTt7FQvRhvj7O+i1vLw+Wrs6mur7N7JDE1zAMg7c+O0JTawd3zhzCIOmVJkSfIMmWEH1cfHQQ98wZRnObjTdc/ZpE77Rl/wkO5laTNjic2enxZocjhOgikmwJ0Q9MHzuQyakxHCup55Ntx80OR1xAWU0L7208SqCfF48sSMNDtrkRos+QZEuIfsBisfDgDSOICfNn9Y4CDudVmx2SOIPd4eDNlVm0dzi4/3pFeLCv2SEJIbqQJFtC9BP+vl5899ZReHlaePPTLOqarGaHJFxWbS/geGkDU0fGMjlVtrsRoq+RZEuIfiQpLpi7Zg2lsaWDP63IxOEwZbsucYa8Ew2s+DKfiBBf7ps73OxwhBDdQJItIfqZOenxjB8WxZHCOj7dnm92OP2atd3OmyszcRgGjyxII8DP2+yQhBDdQJItIfoZi8XCovmpRIb4svzL4+jCWrND6rfe33SM8tpW5k1KIDUp3OxwhBDdRJItIfqhIH9vnlg4CgsW3liRSUNLu9kh9TsHc6vZtK+EQdGB3DEjxexwhBDdSJItIfqpofGh3D4jhbqmdpZ8mo3DkPqtntLY0s5bq7Px9LDw2E1peHt5mh2SEKIbSbIlRD92w5RERiVHcCivmrW7iswOp18wDIN3PtfUN7dz+/QUEmODzQ5JCNHNJNkSoh/zsFh49KY0QoN8+HBLLrkl9WaH1OdtP1zG3pxKhieEcf3kRLPDEUL0AEm2hOjnQgJ9ePzmkTgcBq8vz6S5rcPskPqsyrpWlq3Lwc/Hk0cXpOLhIV3ihegPJNkSQpCaFM7N1wymuqGNt1YfwZD6rS7ncBgs+TSLtnY7984dTlSYv9khCSF6iCRbQggAFl6TzIjEMPbmVLJxb4nZ4fQ5n+8qJKe4nnQVzdWj4swORwjRgyTZEkIA4OFh4bGbRxLk780/Nh6loKzR7JD6jMLyRj7emkdooA8PXK+wyCbTQvQrkmwJIU4JD/bl0ZvSsNkNXlt+mFarzeyQ3F6Hzc6bK7OwOwweXpBKcICP2SEJIXqYJFtCiLOMGRLJjVMSqaht5d01Wuq3OunDLXmUVDUza8IgRqdEmh2OEMIEkmwJIc5z2/QUhgwMYUdWOV8cLDU7HLeVlV/D2t1FxEYE8K1ZQ80ORwhhEkm2hBDn8fL04IlbRhLg68WydTmUVDaZHZLbaW7rYMmqbDwsFh6/OQ1fb+kSL0R/JcmWEOKCokL9WTQ/lXabg9eWZ2LtsJsdkltZtjaH2kYrC68ZTPKAELPDEUKYSJItIcRFpatoZqfHc6Kqmb+vzzE7HLexM6ucHVnlpAwMYcHVSWaHI4QwmSRbQoiv9a1ZQ0mMDWLrgVJ2ZJaZHU6vV9PQxrtrND7eHjx2UxqeHvIxK0R/J58CQoiv5e3lwXdvGYWvjyfvrNGU17SYHVKv5TAMlqzKpsVq4+7Zw4iNCDA7JCFELyDJlhDiG8VGBPDgDQpru53Xlh+mw+YwO6ReacOeYrILahk7JJIZYweaHY4QopeQZEsIcUmmpsUxfewACsubeH/TMbPD6XVKqpr5YHMuQf7ePDQ/VbrECyFOkWRLCHHJvjNnOIOiAtmQUUyGrjQ7nF7DZnfw5opMbHYHD904gtBA6RIvhDhNki0hxCXz9fbkyVtG4uPlwVurs6mqazU7JFPY7A6q6lrRhbV8dbiMJauyKaxoYtqYAUwYHm12eEKIXsZi0lYcRmWlbHIrhLvaduAEb312hCEDQ/i3eyfg5dl3rtsMw6CptYOaBivVDW1UN7RR6/q6xvV9fVM7535yxoT78/xDk/D39TIlbiFE94mODu5UXYB8KgghLtu0MQPILqhlR1Y5H2/N4y432oqmvcNOTaP1VOJUc1YiZaW2oY32iywA8PSwEB7sy7CEMCJDfIkI8SMixI/IEF+GxYdJoiWEuCD5ZBBCXDaLxcL91yvyShv4bGchKjGcMUPM32TZYRg0NLefTqLqnUlUTePphKqxpeOi9w/y92ZAZCARrkQqMsSPiBBf179+hAb64OEhhe9CiMsj04hCiCtWUNbIf767Bz8fL158eDLhwb7d+nytVtt5yVN1/elRqtpGK3bHhT/TvL08iAi+cBJ1MrmS/QuFEBfS2WlESbaEEJ2yIaOYZetyUAlh/Og746945MfucFDX2H5WbVRNg/XU9F5NQxstVttF7x8a5HM6eQo+nUhFhjoTqWB/b2nHIIS4IlKzJYQw1XUTBpFdUMvenEpWfHmcW69NOe8YwzBobrNdIIk6XTNV12TlYtd+vj6eRIX4kTIo5HQS5RqZCg/xIzzIF2+vvlOkL4ToWyTZEkJ0isViYdH8ERSUNbLyy3x8vT3psDuc032nis+tWDvsF7y/h8VCeLAPQweFnpVIhbum+yJDfPH39ZJRKSGE25JpRCFEl8gtqed/lu09r2Yq0M/rrNqo0wmV8/uwIF8pOhdC9GpSsyWE6DWOFNRSXttCZKgfEcHOZMrPRwbQhRDuTZItIYQQQohuZGqBvFLqd0AL0ASMBZ7WWpd15jGFEEIIIfqSzi7fadZa/4fW+r+BfcB/dEFMQgghhBB9RqeSLa31z855rKbOhSOEEEII0bd84zSiUmoNEHuBHz2ntV7hOiYMmAfccalPHB0dfKmHCiGEEEK4rU4XyCulQoHXgP/QWh+/xLtJgbwQQggh3EJnC+Q7NY2olIoCXgF+pLU+rpS65JEtIYQQQoj+oLMNcNa6HmOZUgqgEfiws0EJIYQQQvQV0mdLCCGEEOJrmDqNKIQQQgghvp4kW0IIIYQQ3UiSLSGEEEKIbiTJlhBCCCFENzKrQF4IIYQQol+QkS0hhBBCiG4kyZYQQgghRDeSZEsIIYQQohtJsiWEEEII0Y06u12PcFFKTQZ+Bfjg3MYIwA/w01o/fc6xCcDvgUNa6xd6Ms6upJSaDrwEJAPDtNbtZ/zsf4H7gee01n82KcQeoZQKAU4AM7TWGWbH0136++9bKXUd8ANgOPATrfUKk0PqMZfy2pVSwTg/1zy11g/1bITdo7+8t8+klHoSGAuUAynACa31T8yNqmcopQYCPwLqXTdFAb/RWudf5Pgw4Fat9dvf9NgystVFtNa7gM3Adq31C64k6gVg/QWOLQJW9mR83UFrvRXnay4HHj15u1IqGpiM803aJ0+857gXWA48bnYg3am//7611huBT4Cs/pRowaW9dq11I/BujwbW/frFe/skV3L5C+Ap1znsYSDM1KB6iFIqAPgcePmMc/gvgE+UUkEXuVsY8NClPL6MbHUTpZQX8D/Am0qpvwCHgBHAf2qt81yHpSmlfgSMxvkh9j/mRNtpLwGvKqWWaK2twPeAV4F/A1BKvQmUAEFAqdb6t0qpq4DXgAygDueoyHCtdZ0ZL6CThgH/CmQppZ4BJgB/BHYAxcAk4FWt9Rql1D9wXi2uA64FPtRa/96csK/Ylfy+f4Dzg+t+nBcg7wOH3fWKWSl1A/A6MBPnaPafgLe11m+7fseDcX5wTwT2aK2fNynULvd1r93EsLrLue/tUThf+9NAFs7Xvl9r/YJSKhJ4E9BAE86RwP/UWv/RlMivjBWwAD9USr2jta4CnnSdz34PVAChOF/zu6739X/g/LwLAhTwqNa6xpzwO+V2IMc1GAKA1rpcKbUfuEMp5Yvz9VUBU4H7cCbhg5VSLwCfa613XOzBu2VkSyl1nVLqE6VUllJqYXc8Ry82Syn1e+C3ru//DLyutf41zqu+355xbLvW+tda6weAB5RSo3o41q5yGPgKeFwpFQvYgcozfv6p60rhWeB+pVSw1vornFfK1VrrZ4A5QHNPB95ZSqkpwDatdQXOk+s9rhGg/ThPsr8EngDeUUpZcCYkA4HngfnAanMi75Qr+X3/Eef/SanWugUoAn7a04F3Fa3150C+6+scnCN+J538Hb8ELOSMUcC+4Btee59xkff2Dpx/x7hu/+SMu/wE2Km1/inwByDEzRItXBdPM4BxQLZS6gtXcv0I4KO1fgl4FvgPpVSM6/WFAH9wve6duO/7OgkovcDtZcBNwA+01s9orf8b57ncA2eyne/6vLtoogXdNLKltd6olEoEbupvw+3AJq31s64T6zDgMWCeq97FH+cVz0l5Z3ydC6ThPJG5oxeBNUAi8L84rwBPGqCU+i+gAecbMxJodP0sG0BrfbDnQu1S3waalVJjcV4VPoHzDQiu36/WukwpFQhEu24/prXuADo4/f/gbq7k9/1H4F+UUv8NZGqt+3JH5TyttR1AKdVhdjDiinzde/tCRuK8uEZr3aSUqvyaY3strXUmzoskT5yjPR8Cf8H5vj45En0YiMM50lWptT55XjuGM1lzRwXA+AvcHgfsAXxP3qC1/ieAUir8Uh+8R6YRlVIfA7uBeOBLrfUypdTNwO9wTieE4Jx6uedihWjuxnUiyVFKHQA+0lofdA1D3nbGYSlnfD0UV+LhjrTWWUqprThH66qUUgC4Pqh+rLVOcX1/7kin255wXfUNda6rvZO35Sql0l3fpgAblVIDgBacoz9JuPFrPukKf9+fAP+Nc+RncQ+H3B0acX52gTPpzD/jZ27/O/4GX/fa3d43vLfPfe0nZeFcQICrxicaN6OUGgz8XGv9iNba7jp3v4Hz99t6stRFKXUbp3/n0UqpIFfCNRzn/4M7+gj4sVIq4eRUomvkfiLOXOW+kwcqpe4AtuIc1be4bhuntd5/sQfvqZqtt7XWy12ZcjawTGu90hVwgdb6DVft0h2cPc3mNpRSE4HpgI9S6i6t9QeuHz0CPKOUOg4kAH91rUa8GfBXSj2Ps5brr1rrQ2bEfqXOeM1BSqmfaq3vdd0ejbM2ZwDOEY9spdSfgSPAIOBhpdRfXfcdrZQ6rLXeY8qLuEKuxPl14MwVeSOAGuDXOK/4hiulfo5zfv8hrbWhlHoUSFJKPay1XmpC6FesM79vnKsU7a56roGuYmp3dTKR+hPwnFJqMxAI3KyUWodz2jBJKTUb50k51B1/3xfxda99E86/gzFKqau11ttNirFTLuG9/QLwI1diEgeMUEql4azRXaKU+hXO6Sh3rFuqByKVUr9zfZ2M8+JoKfArV22SD87E62PXfWqAx1wXlQrnOc/taK1blFI3As8qpU6uRgwDFmitC5RS/89VIlQFeGitP3TVsrUppV7GmWReNNnqtr0RlVIP4ZznvBvnUko/oBX4N611uOuYt4F3tNabXMcP1m7cCkGIk1x/229rrTebHEqvoJTy0Vq3uy6qPtJa55od0+VQSs3RWq9XSj0NDNFaf9/smHpKf37tl8OVbPhprY8rpfyAXVrrMWbH1d2UUvla68Fmx9Hb9cTI1gJgrtb6OgCl1Llv1L4+3C76GaXUNGAMzrqHDDcfxekq9ymlxgEV7pZouSx2jVSl4Vp12Y/059d+OSKBXyiltuNcjfr01x/u/pRST+Ecub1Xa73M7Hh6s+4e2VqAszbjfZytD4pxrlT4Ic4Cu9eBfcDPgP8DwoHHtdbHuiUoIYQQQoge1uXJlgw5CyGEEEKc1h3TiDLkLIQQQgjh0m3TiEIIIYQQQvZGFEIIIYToVl0yjaiUGgL8EtiLs3Fptdb6JaVUBM7eI3k4u6n/u2uvofnAXUAmzlVbH7r6cIXj3GPtAM7+NCHAv5zsxCyEEEII4W66qmYrAnhPa70cQDn3RFyFc6ua9Vrr910d43+Ds+ldAs4mh0WuDq05rkQrENiitX7d9TifAbfi3C5ACCGEEMLtdEmypbXefc5NHjg3FV4A/Kfrti+Bd1zHv3HusVprB87WEK+f87Mz9xIUQgghhHArXV6z5dozaY3W+ggQw+mNdhuAcFd7+zP9GDivPYRSapLrvmu7OkYhhBBCiJ7SpcmWUmoWMAv4V9dNFUCw6+sQoFZrbTvj+GeBQ1rrD895nNHA94D7XBs6CyGEEEK4pS5LtpRSC4DrgX8B4pRSVwGrgKtch1zj+v7k8T8DirTWS5VSM5VSka7bJ+FMtB4DDKXUTV0VoxBCCCFET+uSPltKqXRgC7DHdVMg8AqwAvhfoAAYAvzEtRrxBzi36MlyHT8ImAvYcK5QPAjYAU9gg2xOLYQQQgh3JU1NhRBCCCG6kTQ1FUIIIYToRpJsCSGEEEJ0I0m2hBBCCCG6kSRbQgghhBDdSJItIYQQQohuJMmWEEIIIUQ3kmRLCCGEEKIb/X9K3LUtYplwdQAAAABJRU5ErkJggg==\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["df.plot(figsize=(10, 6))\n", "# plt.savefig('../../images/chA/plot_06.png');"]}, {"cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [], "source": ["index = ['2022-01-31', '2022-02-28', '2022-03-31', '2022-04-30',\n", "         '2022-05-31', '2022-06-30', '2022-07-31', '2022-08-31',\n", "         '2022-09-30', '2022-10-31']"]}, {"cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2022-01-31', '2022-02-28', '2022-03-31', '2022-04-30',\n", "               '2022-05-31', '2022-06-30', '2022-07-31', '2022-08-31',\n", "               '2022-09-30', '2022-10-31'],\n", "              dtype='datetime64[ns]', freq='M')"]}, "execution_count": 180, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.DatetimeIndex(df.index)"]}, {"cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 4 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["df.hist(figsize=(10, 6));\n", "# plt.savefig('../../images/chA/plot_07.png');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Input-Output Operations"]}, {"cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": ["df.to_csv('data.csv')"]}, {"cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": [",no1,no2,no3\n", "2022-01-31,-0.8044583035248052,0.3209315470898572,-0.025482880472072204\n", "2022-02-28,-0.16013447509799061,0.020134874302836725,0.363991673815235\n", "2022-03-31,-0.26757177678888727,-0.4598482010579319,0.9590271758917923\n", "2022-04-30,-0.7322393029842283,0.2074331059300848,0.15291156544935125\n", "2022-05-31,-1.9283091368170622,-0.19852705542997268,-0.02946577492329111\n", "2022-06-30,-1.8251162427820806,-0.33694904401573555,0.6762266000356951\n", "2022-07-31,-0.5533209663746153,-1.3236963728130973,0.34139114682415433\n", "2022-08-31,-0.6528026643843922,-0.9165042724715742,1.2607786860286034\n", "2022-09-30,-0.34068465431802875,0.6166567928863607,0.7106048210003031\n", "2022-10-31,-0.7238320652023266,-0.20628417055270565,2.310688189060956\n"]}], "source": ["with open('data.csv') as f:\n", "    for line in f.readlines():\n", "        print(line, end='')"]}, {"cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [], "source": ["from_csv = pd.read_csv('data.csv',\n", "                      index_col=0,\n", "                      parse_dates=True)"]}, {"cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-01-31</th>\n", "      <td>-0.804458</td>\n", "      <td>0.320932</td>\n", "      <td>-0.025483</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-02-28</th>\n", "      <td>-0.160134</td>\n", "      <td>0.020135</td>\n", "      <td>0.363992</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-03-31</th>\n", "      <td>-0.267572</td>\n", "      <td>-0.459848</td>\n", "      <td>0.959027</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-04-30</th>\n", "      <td>-0.732239</td>\n", "      <td>0.207433</td>\n", "      <td>0.152912</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-05-31</th>\n", "      <td>-1.928309</td>\n", "      <td>-0.198527</td>\n", "      <td>-0.029466</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-01-31 -0.804458  0.320932 -0.025483\n", "2022-02-28 -0.160134  0.020135  0.363992\n", "2022-03-31 -0.267572 -0.459848  0.959027\n", "2022-04-30 -0.732239  0.207433  0.152912\n", "2022-05-31 -1.928309 -0.198527 -0.029466"]}, "execution_count": 185, "metadata": {}, "output_type": "execute_result"}], "source": ["from_csv.head() #  <6>"]}, {"cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [], "source": ["h5 = pd.HDFStore('data.h5', 'w')"]}, {"cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": ["h5['df'] = df"]}, {"cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [{"data": {"text/plain": ["<class 'pandas.io.pytables.HDFStore'>\n", "File path: data.h5"]}, "execution_count": 188, "metadata": {}, "output_type": "execute_result"}], "source": ["h5"]}, {"cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [], "source": ["from_h5 = h5['df']"]}, {"cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [], "source": ["h5.close()"]}, {"cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>no1</th>\n", "      <th>no2</th>\n", "      <th>no3</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2022-06-30</th>\n", "      <td>-1.825116</td>\n", "      <td>-0.336949</td>\n", "      <td>0.676227</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-07-31</th>\n", "      <td>-0.553321</td>\n", "      <td>-1.323696</td>\n", "      <td>0.341391</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-08-31</th>\n", "      <td>-0.652803</td>\n", "      <td>-0.916504</td>\n", "      <td>1.260779</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-09-30</th>\n", "      <td>-0.340685</td>\n", "      <td>0.616657</td>\n", "      <td>0.710605</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2022-10-31</th>\n", "      <td>-0.723832</td>\n", "      <td>-0.206284</td>\n", "      <td>2.310688</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 no1       no2       no3\n", "2022-06-30 -1.825116 -0.336949  0.676227\n", "2022-07-31 -0.553321 -1.323696  0.341391\n", "2022-08-31 -0.652803 -0.916504  1.260779\n", "2022-09-30 -0.340685  0.616657  0.710605\n", "2022-10-31 -0.723832 -0.206284  2.310688"]}, "execution_count": 191, "metadata": {}, "output_type": "execute_result"}], "source": ["from_h5.tail()"]}, {"cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": ["!rm data.csv data.h5 # <7>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Case Study"]}, {"cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": ["raw = pd.read_csv('http://hilpisch.com/pyalgo_eikon_eod_data.csv',\n", "                 index_col=0, parse_dates=True).dropna()"]}, {"cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [], "source": ["spx = pd.DataFrame(raw['.SPX'])"]}, {"cell_type": "code", "execution_count": 195, "metadata": {"scrolled": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n", "Data columns (total 1 columns):\n", " #   Column  Non-Null Count  Dtype  \n", "---  ------  --------------  -----  \n", " 0   .SPX    2516 non-null   float64\n", "dtypes: float64(1)\n", "memory usage: 39.3 KB\n"]}], "source": ["spx.info()"]}, {"cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [], "source": ["vix = pd.DataFrame(raw['.VIX'])"]}, {"cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n", "Data columns (total 1 columns):\n", " #   Column  Non-Null Count  Dtype  \n", "---  ------  --------------  -----  \n", " 0   .VIX    2516 non-null   float64\n", "dtypes: float64(1)\n", "memory usage: 39.3 KB\n"]}], "source": ["vix.info()"]}, {"cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [], "source": ["spxvix = pd.DataFrame(spx).join(vix)"]}, {"cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n", "Data columns (total 2 columns):\n", " #   Column  Non-Null Count  Dtype  \n", "---  ------  --------------  -----  \n", " 0   .SPX    2516 non-null   float64\n", " 1   .VIX    2516 non-null   float64\n", "dtypes: float64(2)\n", "memory usage: 139.0 KB\n"]}], "source": ["spxvix.info()"]}, {"cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [], "source": ["spxvix = pd.merge(spx, vix,\n", "                  left_index=True,  # merge on left index\n", "                  right_index=True,  # merge on right index\n", "                 )"]}, {"cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n", "Data columns (total 2 columns):\n", " #   Column  Non-Null Count  Dtype  \n", "---  ------  --------------  -----  \n", " 0   .SPX    2516 non-null   float64\n", " 1   .VIX    2516 non-null   float64\n", "dtypes: float64(2)\n", "memory usage: 139.0 KB\n"]}], "source": ["spxvix.info()"]}, {"cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [], "source": ["spxvix = pd.DataFrame({'SPX': spx['.SPX'],\n", "                       'VIX': vix['.VIX']},\n", "                       index=spx.index)"]}, {"cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n", "Data columns (total 2 columns):\n", " #   Column  Non-Null Count  Dtype  \n", "---  ------  --------------  -----  \n", " 0   SPX     2516 non-null   float64\n", " 1   VIX     2516 non-null   float64\n", "dtypes: float64(2)\n", "memory usage: 139.0 KB\n"]}], "source": ["spxvix.info()"]}, {"cell_type": "code", "execution_count": 204, "metadata": {"scrolled": true}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["spxvix.plot(figsize=(10, 6), subplots=True);\n", "# plt.savefig('../../images/chA/example_01.png');"]}, {"cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [], "source": ["rets = np.log(spxvix / spxvix.shift(1))"]}, {"cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [], "source": ["rets = rets.dropna()"]}, {"cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>SPX</th>\n", "      <th>VIX</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-05</th>\n", "      <td>0.003111</td>\n", "      <td>-0.035038</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-06</th>\n", "      <td>0.000545</td>\n", "      <td>-0.009868</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-07</th>\n", "      <td>0.003993</td>\n", "      <td>-0.005233</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-08</th>\n", "      <td>0.002878</td>\n", "      <td>-0.050024</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-11</th>\n", "      <td>0.001745</td>\n", "      <td>-0.032514</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 SPX       VIX\n", "Date                          \n", "2010-01-05  0.003111 -0.035038\n", "2010-01-06  0.000545 -0.009868\n", "2010-01-07  0.003993 -0.005233\n", "2010-01-08  0.002878 -0.050024\n", "2010-01-11  0.001745 -0.032514"]}, "execution_count": 207, "metadata": {}, "output_type": "execute_result"}], "source": ["rets.head()"]}, {"cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [], "source": ["rg = np.polyfit(rets['SPX'], rets['VIX'], 1)"]}, {"cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["rets.plot(kind='scatter', x='SPX', y='VIX',\n", "          style='.', figsize=(10, 6))\n", "plt.plot(rets['SPX'], np.polyval(rg, rets['SPX']), 'r-');\n", "# plt.savefig('../../images/chA/example_02.png');"]}, {"cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [], "source": ["ret = rets.mean() * 252"]}, {"cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [{"data": {"text/plain": ["SPX    0.104995\n", "VIX   -0.037526\n", "dtype: float64"]}, "execution_count": 211, "metadata": {}, "output_type": "execute_result"}], "source": ["ret"]}, {"cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [], "source": ["vol = rets.std() * math.sqrt(252)"]}, {"cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [{"data": {"text/plain": ["SPX    0.147902\n", "VIX    1.229086\n", "dtype: float64"]}, "execution_count": 213, "metadata": {}, "output_type": "execute_result"}], "source": ["vol"]}, {"cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [{"data": {"text/plain": ["SPX    0.642279\n", "VIX   -0.038667\n", "dtype: float64"]}, "execution_count": 214, "metadata": {}, "output_type": "execute_result"}], "source": ["(ret - 0.01) / vol"]}, {"cell_type": "code", "execution_count": 215, "metadata": {"scrolled": true}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(10, 6))\n", "spxvix['SPX'].plot(label='S&P 500')\n", "spxvix['SPX'].cummax().plot(label='running maximum')\n", "plt.legend(loc=0);\n", "# plt.savefig('../../images/chA/example_03.png');"]}, {"cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [], "source": ["adrawdown = spxvix['SPX'].cummax() - spxvix['SPX']"]}, {"cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [{"data": {"text/plain": ["579.6500000000001"]}, "execution_count": 217, "metadata": {}, "output_type": "execute_result"}], "source": ["adrawdown.max()"]}, {"cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [], "source": ["rdrawdown = ((spxvix['SPX'].cummax() - spxvix['SPX']) /\n", "              spxvix['SPX'].cummax())"]}, {"cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [{"data": {"text/plain": ["0.1977821376780688"]}, "execution_count": 219, "metadata": {}, "output_type": "execute_result"}], "source": ["rdrawdown.max()"]}, {"cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [], "source": ["temp = adrawdown[adrawdown == 0]"]}, {"cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [], "source": ["periods_spx = (temp.index[1:].to_pydatetime() -\n", "               temp.index[:-1].to_pydatetime())"]}, {"cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([datetime.timedelta(days=67), datetime.timedelta(days=1),\n", "       datetime.timedelta(days=1), datetime.timedelta(days=1),\n", "       datetime.timedelta(days=301), datetime.timedelta(days=3),\n", "       datetime.timedelta(days=1), datetime.timedelta(days=2),\n", "       datetime.timedelta(days=12), datetime.timedelta(days=2)],\n", "      dtype=object)"]}, "execution_count": 222, "metadata": {}, "output_type": "execute_result"}], "source": ["periods_spx[50:60]"]}, {"cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.timedelta(days=417)"]}, "execution_count": 223, "metadata": {}, "output_type": "execute_result"}], "source": ["max(periods_spx)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n", "\n", "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"]}], "metadata": {"anaconda-cloud": {}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6"}}, "nbformat": 4, "nbformat_minor": 4}